{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1d51e31",
   "metadata": {
    "papermill": {
     "duration": 0.052042,
     "end_time": "2021-09-18T10:51:56.464794",
     "exception": false,
     "start_time": "2021-09-18T10:51:56.412752",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Bayesian Hierarchical Stacking: Well Switching Case Study\n",
    "<p></p>\n",
    " <figure>\n",
    "  <img src=\"https://i.imgur.com/CiUmZKx.jpeg\" width=\"700px\"/>\n",
    "  <figcaption>Photo by Belinda Fewings, https://unsplash.com/photos/6p-KtXCBGNw.</figcaption>\n",
    "</figure> \n",
    "\n",
    "## Table of Contents\n",
    "\n",
    "* [Intro](#intro)\n",
    "* [1. Exploratory Data Analysis](#1.-Exploratory-Data-Analysis)\n",
    "* [2. Prepare 6 Different Models](#2.-Prepare-6-different-candidate-models)\n",
    "    * [2.1 Feature Engineering](#2.1-Feature-Engineering)\n",
    "    * [2.2 Training](#2.2-Training)\n",
    "    * [2.3 Estimate leave-one-out cross-validated score for each training point](#2.3-Estimate-leave-one-out-cross-validated-score-for-each-training-point)\n",
    "* [3. Bayesian Hierarchical Stacking](#3.-Bayesian-Hierarchical-Stacking)\n",
    "    * [3.1 Prepare stacking datasets](#3.1-Prepare-stacking-datasets)\n",
    "    * [3.2 Define stacking model](#3.2-Define-stacking-model)\n",
    "* [4. Evaluate on test set](#4.-Evaluate-on-test-set)\n",
    "    * [4.1 Stack predictions](#4.1-Stack-predictions)\n",
    "    * [4.2 Compare methods](#4.2-Compare-methods)\n",
    "* [Conclusion](#conclusion)\n",
    "* [References](#references)\n",
    "\n",
    "## Intro <a class=\"anchor\" id=\"1\"></a>\n",
    "\n",
    "Suppose you have just fit 6 models to a dataset, and need to choose which one to use to make predictions on your test set. How do you choose which one to use? A couple of common tactics are:\n",
    "- choose the best model based on cross-validation;\n",
    "- average the models, using weights based on cross-validation scores.\n",
    "\n",
    "In the paper [Bayesian hierarchical stacking: Some models are (somewhere) useful](https://arxiv.org/abs/2101.08954), a new technique is introduced: average models based on weights which are allowed to vary across according to the input data, based on a hierarchical structure.\n",
    "\n",
    "\n",
    "Here, we'll implement the first case study from that paper - readers are nonetheless encouraged to look at the original paper to find other cases studies, as well as theoretical results. Code from the article (in R / Stan) can be found [here](https://github.com/yao-yl/hierarchical-stacking-code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2d43427d-0ac3-4383-8441-375164cbecb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a71e927",
   "metadata": {
    "papermill": {
     "duration": 4.069199,
     "end_time": "2021-09-18T10:52:00.594720",
     "exception": false,
     "start_time": "2021-09-18T10:51:56.525521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import arviz as az\n",
    "from IPython.display import set_matplotlib_formats\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.interpolate import BSpline\n",
    "import seaborn as sns\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "import numpyro\n",
    "import numpyro.distributions as dist\n",
    "\n",
    "plt.style.use(\"seaborn\")\n",
    "if \"NUMPYRO_SPHINXBUILD\" in os.environ:\n",
    "    set_matplotlib_formats(\"svg\")\n",
    "\n",
    "numpyro.set_host_device_count(4)\n",
    "assert numpyro.__version__.startswith(\"0.19.0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "227f2ff1-63f3-4529-89ba-4c92fc7bb518",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255e8d79",
   "metadata": {
    "papermill": {
     "duration": 0.043256,
     "end_time": "2021-09-18T10:52:00.780796",
     "exception": false,
     "start_time": "2021-09-18T10:52:00.737540",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 1. Exploratory Data Analysis <a class=\"anchor\" id=\"1\"></a>\n",
    "\n",
    "The data we have to work with looks at households in Bangladesh, some of which were affected by high levels of arsenic in their water. Would affected households want to switch to a neighbour's well?\n",
    "\n",
    "We'll split the data into a train and test set, and then we'll train six different models to try to predict whether households would switch wells. Then, we'll see how we can stack them when predicting on the test set!\n",
    "\n",
    "But first, let's load it in and visualise it! Each row represents a household, and the features we have available to us are:\n",
    "\n",
    "- switch: whether a household switched to another well;\n",
    "- arsenic: level of arsenic in drinking water;\n",
    "- educ: level of education of \"head of household\";\n",
    "- dist100: distance to nearest safe-drinking well;\n",
    "- assoc: whether the household participates in any community activities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "01d1703b",
   "metadata": {
    "papermill": {
     "duration": 0.078754,
     "end_time": "2021-09-18T10:52:00.905455",
     "exception": false,
     "start_time": "2021-09-18T10:52:00.826701",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "wells = pd.read_csv(\n",
    "    \"http://stat.columbia.edu/~gelman/arm/examples/arsenic/wells.dat\", sep=\" \"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2bf6c000-cb9a-4c81-898f-5ac4cdd1020a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>switch</th>\n",
       "      <th>arsenic</th>\n",
       "      <th>dist</th>\n",
       "      <th>assoc</th>\n",
       "      <th>educ</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.826000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.71</td>\n",
       "      <td>47.321999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2.07</td>\n",
       "      <td>20.966999</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1.15</td>\n",
       "      <td>21.486000</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1.10</td>\n",
       "      <td>40.874001</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   switch  arsenic       dist  assoc  educ\n",
       "1       1     2.36  16.826000      0     0\n",
       "2       1     0.71  47.321999      0     0\n",
       "3       0     2.07  20.966999      0    10\n",
       "4       1     1.15  21.486000      0    12\n",
       "5       1     1.10  40.874001      1    14"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wells.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5dec77a2",
   "metadata": {
    "papermill": {
     "duration": 1.122344,
     "end_time": "2021-09-18T10:52:02.072825",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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pZyytYs2yKnr7+hkYgL6+AXx3G/V721m/ci62uJzHtwbbtWZpFWetqGbzrlY2bG9i+aJy1tRVUVxUwKYdLXhjK2fUVbGgehazZhRTv6edTTtbWF1byZnLqtm0s4XNu1pYv6LmhP21tq6a2vlz+OXDu1m6sIz1K2po6ehiz8HDQZtLq4J99HAji0+bwzmrTqPrWC9bdrWyfW87q8L92tLRzeLTZvPHTft54XmLB/d74r26+48NLKiZzfzqWTS3dbFyScXg/k28N4eO9rBiUTlPPPYMWxtaOXN5NWcuqzphX55jNdTv7aB89nSmTytka0Mr9XvbsSWVLKyZzawZRdTv7WDzrhbW1FUxr3IWB9uOcubyahqeaWf69GKa27qorpjB/uYjNOzr4IoLFrNrXyebdrawfFE5q2ormDGtmK0NrWza2cyZy6pZv6KaOSWFHOru58ntzWzb086VieV2tHDmsirWr6hm9vTgRNDe1c9jG/YPxn3uqrlUzCyks6ufTTtbKZsznf3NR/DG1qD9ldXEgCe2NbNxSHvxFDkxFoPDx4JYhs6fav1nrajm7gcbqZ0/J2O7Yy3TdoxmfGO1nqmqsBBaj/azcdOznH7abKrLpzMwAB1Hetna2Eb9njbW1lWzckkFT+8M8lUiN27c0cLGHc2cmTR9a2Mra+uqOKOuiq0NbTxVH+Q8W1xBYVGM/r4BSmdP5wlvYte+Dq44fzE793WyZVcLVlvJmqVVeGMrT+9s4Yy6KtYsrWJaYYzOrl4272plz7OHuPKCJezc18HmcJ51y6sHc82aZVXULQjy3tIFZUF+OtLDsvA8tGlnS5AbF5Ry/xPPcMk5C2lIyiGrayuZNaOYDdub2NIQbMuyReUcPtrL3qZDbGtsY82yamrnJeXelTXsOdDJ4vmlPPXwbnY/e4hrLqo9IZeuqatiIB5n+552NoXnHltSQUEsxtM7W9j1TJDPGvZ38vTOFtbUVXNGXSVP72xh886Wk96DNSn28aolFXT39HOg5Qg1FTM5dKSHuoVlPLmtie172gbz9eEjz7Ls9HKe3NbE1sbWwXNBa2c3K04v59mWw5w+r4wnntrPloZg+qK5s5k5vSg4d+5s4cxl1Zy5vJoj3b3s3NvBisUVg+d5q61g3YoaZk4vYGZ4pTHVZzg5L2/c0cLaZVUsXVDKvQ/tZlVtBWctq8r6cz7Z8sRki3ciisXzuKfM7ALgk+5+2ZDXXwp8GOgDbnf3Lw/XVpT+dR3H+jna3cv04kI+8Y1Hefdrz+Gz//s4LR3dg/NUlZXw7teew96Dh7njvm0nTXv/m57DJ77x6Emvv/qFK7njvm28+oUr+eIPnxp8LdPjxLLXXbmKJfPnUL+3I+U6X/3ClQBp40m33M3XncOB5qN8596tg9NuesVZadeRiOkdr15/wjLJcX7+jidTtjNcu+nazGb6HfdtS7vf3/3ac/jgFx9M+R4kz5P8PqeL9f1veg6N+w8Nu+3pYks8b+noTruO665cRW//wAnxfejNF/BPtz+ccblb/uoC+uMMzpc87YNvvoCP3/7wCesfLvZb/uoCZk07+auqIz393PLVk9eRaf3J70G6djOpqZlDU9OhSMsMJ9N2RI1vtNeTvL2TqU/0aOXtqDk7cWwvX1QGcNJndLjck03OS3xO5lXP5DPfeTzjZ3Hocsm5KWpeTJWfEvOke31oDhkut1aVlfCPN5zPP3/tEVo6uvn4Tc9N2e7N153DP37hwcHX/vltz816X0TZx739A9xx37a02zfcOTZ5WxIynbNOq5qZdntLZxQDpPwMJ+fldPs123wyVvko2Ujy6ljFm4/cP5rSxTeufaLN7D3AV4CSIa8XA58FrgQuBf7azE4brfWWlk5j77OHONLVw4btzcytmMHWhtYTDhKAlo5uvLGVWIyTphUWxNiwvTnlMi0dXRQWxGjp6GJuxYzB1zI9Tix7oPUIzzQd5tDRnpRtt3V203WsL+W0+r3tdB4+lnLa7gOdPNt2ZHDa3IoZNLd3pY1/bsUM5lbMYH/LkZTzHGg9MjhPcjvDtbu6tiJtm9lMt8Xlafe7N7ayurYi5XuQeM+2NrZmFetT9c0c7k79HiS2Pfm1ts7u4Jiobz6h3bkVM2jtTL2OA61H6O7pO+H9f9wPUlgQyxjbhvpmtqQ5Xp/wgxmXTRX7hvrmwW4jCQUFMZ5Ms5+37WnncT847HuQqt2xlmk7RjO+sVrPRDAeeXvatEIe3xp8NmaWFNHS0UX93vYTckU2uWe4nJd4fqirh60NbYOf4WyXeyr8/EfNi3MrZpyUnxLSvT40h2TK18nnnCe3NVFYEGN1bUXa897WhuBzDLC6toItu1qz2hdR9nFy7Om2b0N9M4VDPjvJ+T2xLQmZ9kF/fCDterY2tLL74GG27WlPuf8f25o63yXv12w+55MtT0y2eCeqfHbn2AG8AvjWkNdXA/Xu3gZgZn8ALgHuyNRYRcXMrDt/9/XHKS0uHuzy4I1tKefzxjYuXDv/pNcX1Mxma0NrymUa9ncyv3r24P8H27qyepxYdkH1LLbtTh3Pzn0dLJ1fmnJaW+cxdu7rSDmt/VAPu57pPCH+xv2dKedNxBSLEXme4dpdvbRqRNMvWDOPhzcdSDndG9tYXVvFloa2lO/BgprZeMPx/Zop1q2NrdTOS72fh75nELwv86tn4w2tJ2zDgprZJ+z3oe0sXVB6QltbG1qH3febdrSweN6c1HEPWX82sW/a0cLLL11+0rwbd7akbKO5vYv6Z1IfZ8nvQbp2h1NTk3rbcpVuO3KNb7TXM9rbOwZGLW9HydlbG1pZUDObslnTAWjr7BzV3JP8uejq7hvMpdnkysHPb2Mrq2ur2LmvI1Jsic98cn5KXjbV64llEzkkm3x9sK1rMMcsX1SW8byX+Bwnnx9Hcx8nYl+9tCrt9nkYa3K+Sm5r65DpmeIrLixMv57GNqrKSjjY3nXStGzO9QfburLOJ2OVj4bKNc+MZbwTPRfmGl/eimh3/4GZ1aaYVAokn6UPAWXDtZft8CilpdMoKoxxrLeXVbWV/GnLAc5ddRqPbnn2pHltSQW9/QMnvb6v6TBXXVibcpna+aX87vG9XHrOIn73+N4TXsv0OPG8oCDGysUVPLr55LbrFpRRMj31W1JROp0VBeUplyufM42lC0sH493XdJhLzl6UMX4g8jzDtbtpRzNr6qpznv741mdZVVuZ9r36U/h6qvdgX9NhrrxwSVaxrlpSSWFh6r+yh75nELwvv3lsD1ddVMufNh8Y3IZ9TYe57Nz0+2NmSTH7mw8fX29tJXf/sQFIv+/XLqtK+4OWVbWVPJq0/mxiX7usipaWwyf9gObMuioeefrkP1iqy2dQXFyY8jhLfg9StTuc0f5KL9N25BLfaK9nSHeOEccxFkYzb2ebs6dNK2RVbSX3PNRAx5FjTC8uoKJ0OksGss9pw+WW5M/FjJIiVi2p5NHNz2adK4FwmQO0dHRHyouJNpLzU8LQvDV02eQcks06Ejmmp7cv43kv8TnevKtlcL7R3MeJ2B/etJ9zV6eJo7aSe8J8mKqtqy+qHcyXkHk/9/b3Y7UVabe3uKiQueUzTpqWzbkesssnY5WPhso1r45lvJO4O8ewy47HEHedQHJkc4D2UWu8s4dFp81h1oxprFtRzcG2LlbVVlJVdsK3k1SVlWBLKonHOWla/0CcdSuqUy5TVTaD/oE4VWUzONjWNfhapseJZedVzmJhzWzmzJyWsu2K0hJmTC9KOW35onJKZ09POW3xvFJOq5g1OO1gWxfV5TPSxn+wrYuDbV3Mr5qVcp7gx3RdJ7UzXLtbGtrStpnNdN/dnna/25JKtjS0pXwPEu/ZqiWVWcV61vJqZpekfg8S2578WkVpSXBMLK8+od2DbV1UlqZex7zKWZRMKzrh/T/H5tI/EM8Y27rl1axOc7yebXMzLpsq9nXLq09KhAMDcdan2c8rTy/nHJs77HuQqt2xlmk7RjO+sVrPBJe3vN3T0885q4LPxtHuPqrKZrB8UfkJuSKb3DNczks8nzNjGqtqKwY/w9kud1b4+Y+aFw+2dZ2UnxLSvT40h2TK18nnnPUra+gfiLOloS3teW9VbfA5BtjS0MbqpZVZ7Yso+zg59nTbt255Nf1DPjvJ+T2xLQmZ9kFhrCDtelbVVrJ47mxWnl6ecv+fuyp1vkver9l8zidbnphs8U5U+f5hYS3wXXe/MOm1YmAzcAFwGPgj8DJ3fyZTW1F+pJIYnSMejwejczS0UbewjM27jo/Oceayau5+qIFlC8tZsbicJ5NG51i2sIxZ04PROTbuaGZrQxu2pIJ5VbNo6+xm8bw53PtwIysWlbM0HHli2aJyli4o5b5H9lC7oJQzllaxbXcbT+9sYfmiMlYuqaBs1jS27W5n044WrrhgCQ37OsJfaVeyZF4p9/1pN2fUVrGqtoInth0fUWL9ihp++Ugj86tns6auiqfqm9gaJr81dSeOzrFtdxv1z7Rz9oq5rFhcfsKoI2ctr2bLrlY21DexfGE5Z4SjcwS/tG5jTV0l86qC0Tl27O1g085mVi+pZO2yap7elRidYy4rTi/nyW0Hqd/bzpq6apbMn8Ovwl+Ir1teQ0tnF3sPHg6+NlxaSd2CMn75SDA6x9l2Gl3H+tja0EL93g5sSQXzq2bR0tnN6afN5uFNB3jBuaeH+/34e3XPQ40sqJnF/KpZNLV3sXJxxeD+Tbw3R472HP+1dzg6x9q6qhP25dkra9jxTAdls6czvbgQb2wdjGNhzWxmlhSx45lwdI6lVcyrmsWzrcHoHI372pk+rZim9i5qyoP+eQ37Orn8/NNp2H8oGIllYTA6R8m0YraGv+4/c1nwy/3Er8A31DezfXc7l1+wmIZ9wa/g14bzJI+O8bgfTBpV5PjoHE/vaqVs9nT2txzBG9uC9lcEo3M8ub2ZTTtObC/T6Bwb6k+eP9X6z1xezb1/bGTJ/DkZ280kH1cjMm1HPkbniLKeSfzDwlpGIW9HydmDo3PUN3P63BNH5/DdbdTvaWdNXRUrF1eweVeQr1bVVrB+RQ2bdrawaUfLSdPX1FUG3Qkaw5EjFpazcnEFRUXBcJmls6fz5LYmGvZ18sLzT2fXvk627GrFllQE+buxjad3tQy2M60gGJ1jy65W9hw8xBXnL2HXvg4272rljKXBKEKJXLOmLhiV6JcP76Z2QSnzq4LRg4LzUDBqzxlLg1Egfv/EPp5/9gIa94ejcywsZ/XSSmaWFPPU9ia2NLaytq6aZYvKOHS0l2cOHmbb7jbW1lWxODn3rqhh77OdnD6vlKfqm9nz7CGuvqiWjUNG5+gfiFO/p52ndzazemmwzwoKYmwOR+e4/PzFNB44FOSluipWLa1k884WNu9qTbGPq1i9tPKEfWy1FRw71s+BlqNUV8zg8NEeli4o48ntTdTvaR/M+YeO9rBsUTkbtjfhjW2Dr7cd6mbZouOjczzpB9kaTl9UM5sZJUXsCvPmmcuqWLssGJ1j1zNDRudYEowqNHR0jqGf4eS8nDiOaheU8quHd2O1lZxZVxl5dI5856NkI8mrYxXvJL4SPWzeHrMi2syuA2a7+38n/cq7gOBX3v81XFu53P1qMow1mkpivM18mAjjRMdiBYPdKRLjRE+bNo2ent4TxomePr2IWOzkcaJjMcLxTIcfJ7qoqJDi4uzGie7vD8YrHYtxoquqZtPSEnxNO1XGic5nIp2I40SfCkX0SPJ2Ljl72rRCyspmcuzYsQmdu/OZo9OJMk70rFnT6e3ty+s40X198TCPnzxOdPDZyDxOdHn5zKQYJ+Y40eXlM3PuKjFW4y6PRl7Nd7wqoieAqXTb74l+wOXDVNvmqba9MPW2ebIW0aMl15w9GY6TiR7jRI8PFONoUYwjNyGHuBMREREROVWpiBYRERERiUhFtIiIiIhIRCqiRUREREQiUhEtIiIiIhKRimgRERERkYhURIuIiIiIRKQiWkREREQkIhXRIiIiIiIRqYgWEREREYlIRbSIiIiISEQqokVEREREIlIRLSIiIiISkYpoEREREZGIVESLiIiIiESkIlpEREREJCIV0SIiIiIiEamIFhERERGJSEW0iIiIiEhERflq2MwKgNuAdcAx4EZ3r0+a/vfAdcAA8C/ufme+YhERkcyUs0VEosnnlehrgRJ3vwh4H/DpxAQzKwf+DrgIuBL4jzzGISIiw7sW5WwRkazl7Uo0cDFwN4C7P2Rm5yVNOwI0ArPCfwPDNVZRMZOiosJ8xDkh1dTMGe8QxtxU2+aptr0w9bZ5km3vhMnZk2G/TfQYJ3p8oBhHi2IcuVzjy2cRXQp0JD3vN7Mid+8Ln+8BNgOFwCeGa6yt7ejoRzhB1dTMoanp0HiHMaam2jZPte2FqbfNyds70U8goQmRsyfDcTLRY5zo8YFiHC2KceTSxZdN3s5nd45OIDmCgqRkfA0wH1gKLAauNbPz8xiLiIhkppwtIhJBPovoB4AXAZjZhcDGpGltQBdwzN27gXagPI+xiIhIZsrZIiIR5LM7x53AFWb2IBADbjCzm4F6d7/LzC4HHjKzAeAPwC/zGIuIiGSmnC0iEkHeimh3HwBuGvLy1qTpHwE+kq/1i4hI9pSzRUSi0c1WREREREQiUhEtIiIiIhKRimgRERERkYhURIuIiIiIRKQiWkREREQkIhXRIiIiIiIRqYgWEREREYlIRbSIiIiISEQqokVEREREIlIRLSIiIiISkYpoEREREZGIVESLiIiIiESkIlpEREREJCIV0SIiIiIiEamIFhERERGJSEW0iIiIiEhEKqJFRERERCJSES0iIiIiEpGKaBERERGRiIry1bCZFQC3AeuAY8CN7l6fNP0a4CNADHgMeLu7x/MVj4iIpKecLSISTT6vRF8LlLj7RcD7gE8nJpjZHOBTwEvc/QKgAajOYywiIpLZtShni4hkLRaP5+dCgpl9BnjE3b8bPn/G3ReGj68Crgd6gDrgK+7+jUzt9fX1x4uKCvMSq4hInsXGO4DhKGeLiJxg2Lydt+4cQCnQkfS838yK3L2P4ArGC4D1wGHg92b2R3fflq6xtrajeQx1YqmpmUNT06HxDmNMTbVtnmrbC1Nvm5O3t6ZmzjhHk5UJkbMnw3Ey0WOc6PGBYhwtinHk0sWXTd7Oqog2sxcT9IWrIqjMY0Dc3esyLNYJJEdQECZjgBbgUXc/ELZ/P0FyTpuQRUQkO8rZIiL5l+2V6FuBvwOeBrLt//EA8FLge2Z2IbAxadrjwFozqwbagQuBL2fZroiIZKacLSKSZ9kW0e3u/rOIbd8JXGFmDxJcBbnBzG4G6t39LjN7P3BPOO/33H1TxPZFRCQ15WwRkTzLWESb2SXhwy1m9jngR0Di6z3c/f50y7r7AHDTkJe3Jk3/LvDdiPGKiEgaytkiImNnuCvRH016vAg4M+l5HPizUY9IRERypZwtIjJGMhbR7v6CxGMzm+vuB81sJrAgeRB+EREZf8rZIiJjJ6ubrZjZO4C7w6c1wE/M7K/zFpWIiORMOVtEJP+yvWPhW4HnA7h7I3Au8I58BSUiIiOinC0ikmfZFtHFwLGk5z1kP2ySiIiMLeVsEZE8y3aIux8Bvzaz74XPXwHclZeIRERkpH6EcraISF5ldSXa3d8LfB4woA74nLt/MJ+BiYhIbpSzRUTyL9vbfn/e3d8B3JH02jfc/U15i0xERHKinC0ikn/D3WzlKwRXMc4zszVDlivPY1wiIhKRcraIyNgZ7kr0x4Fa4FZOHMS/D9iSp5hERCQ3ytkiImNkuD7R3e7+W+ClwK6kf3uA2fkNTUREIlLOFhEZI8Ndif4K8BLgdymmxQm+NhQRkYlBOVtEZIwMd9vvl4QPL3D3g2MQj4iI5Eg5W0Rk7GQ7TvRvzKwT+BnwU3d/Mn8hiYjICClni4jkWbbjRK8BXgu0AB81sy1m9oW8RiYiIjlRzhYRyb+simgzKwCqgVnhMtPC5yIiMsEoZ4uI5F+23TnagSPAfwIfdPcNeYtIRERGqh3lbBGRvMq2iH4l8ELgGuAqM/s98Ft3/2XeIhMRkVwpZ4uI5FlWRXSYeH9pZuXAnwMfAN4JzMlfaCIikgvlbBGR/MuqiDazfwX+DCgD7gbeTupxSJOXKQBuA9YBx4Ab3b0+xTw/A37s7l+MHL2IiJxEOVtEJP+y+mEh0An8GDgLWA18m2BA/0yuBUrc/SLgfcCnU8zzcaAiyxhERCQ7ytkiInmWbZ/olwHvAV4BHAXOAX4I/CDDMhcTXAHB3R8ys/OSJ5rZq4CBxDzDqaiYSVFRYZbhTn41NVPvW9epts1TbXth6m3zOG7vpM7Zk+E4megxTvT4QDGOFsU4crnGl20RXeDu95vZt4EfuPseMxtu2VKgI+l5v5kVuXufma0FrgNeBXw4mwDa2o5mGerkV1Mzh6amQ+Mdxpiaats81bYXpt42J2/vOJxAJm3OngzHyUSPcaLHB4pxtCjGkUsXXzZ5O9vuHEfN7O8J+tj91Mz+Dhhuj3Ry4o9YCty9L3z8RmAh8GvgeuBmM7s6y1hERCQz5WwRkTzLtoh+HcGg/a909zZgAcFViUweAF4EYGYXAhsTE9z9Pe5+gbtfBnwd+Iy7Z/UVoYiIDEs5W0Qkz7Id4u4Z4GNJz9+bxWJ3AleY2YNADLjBzG4G6t39rlyCFRGR4Slni4jkX7Z9oiNz9wHgpiEvb00x3y35ikFERLKjnC0iEk223TlERERERCSkIlpEREREJCIV0SIiIiIiEamIFhERERGJSEW0iIiIiEhEKqJFRERERCJSES0iIiIiEpGKaBERERGRiFREi4iIiIhEpCJaRERERCQiFdEiIiIiIhGpiBYRERERiUhFtIiIiIhIRCqiRUREREQiUhEtIiIiIhKRimgRERERkYhURIuIiIiIRKQiWkREREQkoqJ8NWxmBcBtwDrgGHCju9cnTX838Jrw6c/d/aP5ikVERDJTzhYRiSafV6KvBUrc/SLgfcCnExPMrA54HfBc4ELgSjM7K4+xiIhIZteinC0ikrV8FtEXA3cDuPtDwHlJ0/YAV7t7v7vHgWKgO4+xiIhIZsrZIiIR5K07B1AKdCQ97zezInfvc/deoNnMYsCngCfcfVumxioqZlJUVJjHcCeWmpo54x3CmJtq2zzVthem3jZPsu2dMDl7Muy3iR7jRI8PFONoUYwjl2t8+SyiO4HkqArcvS/xxMxKgNuBQ8DfDNdYW9vRUQ9woqqpmUNT06HxDmNMTbVtnmrbC1Nvm5O3d6KfQEITImdPhuNkosc40eMDxThaFOPIpYsvm7ydz+4cDwAvAjCzC4GNiQnh1YwfAxvc/a3u3p/HOEREZHjK2SIiEeTzSvSdwBVm9iAQA24ws5uBeqAQuBSYbmbXhPO/393/mMd4REQkPeVsEZEI8lZEu/sAcNOQl7cmPS7J17pFRCQa5WwRkWh0sxURERERkYhURIuIiIiIRKQiWkREREQkIhXRIiIiIiIRqYgWEREREYlIRbSIiIiISEQqokVEREREIlIRLSIiIiISkYpoEREREZGIVESLiIiIiESkIlpEREREJCIV0SIiIiIiEamIFhERERGJSEW0iIiIiEhEKqJFRERERCJSES0iIiIiEpGKaBERERGRiFREi4iIiIhEVDTeAeRTaek0iorGfxMHBuIUFMQiLVNZOTNcFiDOwMAARUWFJ83X3z9AYWEBAwNxYrEY8Xiwrv7+gfA5DAz0U1BQAMSIxeIUFBQMxpSYXlhYSDweJx6HeDweth4jFguex2IFxOMD4eNgPf39cQoLg+0Kti9GT08/fX39FBcXUVgIsViMvr4BBgagoAAGmwaKiwvp7++nt3cAgOnTg/eqt7cfgMLCgsH19fcPDMY8bVohBQUF9PcPUFAQo7e3n76+4HFhYcEJ8xYXFw7G29vbz8BAnKKiAoqLC9Mul9ieoa8NlZhnaIxDp2dqI9+ixjCSmCfC9sqpobR0OoWFhcSipc0xlcjR2YjHyXpb+vuDfHxyG3ESOTk5fwc5+uT2y8tnAMEyieWD80Awb39/kHcLCwvC9Q6EeZ3B3A1x+vriFBXFKCgI8nUsVhC2Gw/zeSyMJVhXPN4PFNDT0z94PkqsIzgXDDBtWpDrZ8+eTiwWnF8GBgYGczXEBs8DBQUFxGLQ19dPYWEBRUUF9PfHB9tObHt/f5yenr7wHFEUntsG6O3tp6AgxvTpxYPzDQwMDJ4jh+b94uJg3x9ff/bnguRzT6bnU1XyfgDGZJ/ke9/nrcI0swLgNmAdcAy40d3rk6a/BXgr0Ad83N1/OlrrLiqCliP9/Ob3jWxtaGXtsirWLqtm445mNu9s4Yy6KmrnlfKrR3ezqraS1bWVbNjexM5nOrjygiXs3NdB/e52rrhgMQ37O3l6ZwtWW8mapVXs3t/J4vmlPL2zha0NrayqrWT9yhp6+wfY2tDKgeYjXHnBEp7Y1sTWhlbWLKuibkEZ9z7UyPLF5cHjhxupW1jGmcuq2byrhU07W1iztIqzVlSzaUcLG3c0s2pJJfOrZ9Hc1kV1xQxaO7uZXzWTmrIS2g73sLWxjfq97axdVs2ZdVXHl6ut5ByrYcuuNjbvauHKC49vT+Lx0ztbBtt/tuUIyxaVM2dGMYe7e6nf28GOve1c/pzFNBwItn11bSVnLa+m4/Ax9hw8zNM7W1i+qBxbXEFxcSGzphfymDcN7o+zV9bQ3NHOnmcPs7WxlbV1VdTOL+WXD+9m5eIKzlxexRPbmtiyK5j/nFU17G46wlP1zYPbtGxBGUe6e9l78DDe2MrZK+eyemklG+qb2bSjmeWLylm1pIIZ04tp2NfBiiUVbKpvZuOOFs5cVsWZy6vZvKuVJ7YdHIx1+vRCSqYV8cRT+9myq5XVSys52+aydVcrT2xr4sxlVaxfWU0MeGLb8bbWr6hm9vTCwT8AYjE4fKyfJ7c3s3FHMxbuy45Dx1hbV8mckkIOdSemp24j306McfgYos4/WsvKxDGeORuCwq2jq599YS7evruNFz+/jmlFBTwZ5lNLyotrl1WxYXsTmxtaWb+ihtW1lRzu6qV+bzvzqmaxv/kI3tiK1VYyv2oWB1qOsOL0cmZOL+axrc+y+9lDXHnBEnY8E+S8RO5/ekcLq8Kc94s/NlA7r5SVSyqCnN/YyqollZxtNRQC7UeScnFdNWetqOLw0V527etk1ZJKuo71Jk0PzkNHu3vZvKuV+r3trFlaxbyqWTR3dLGgahZzZk/jqe3Ng7n0rOXV3Pfobp63bgHxOGxpaB1sq25hOfc+1MDyxRWsXVrF0WNB/t68K8jPq2or6D7WT8ehY9iScg519dHXN4DvDuJZv6KGM5ZW8rifeK765cONLDu9nNp5pdz36G5qF5QxvzrYf8sWllE2axpP7Whh2+42rr6oloGB+GBcZyytYvnCMmbPLKbxQCenzy3lUFcPnrQPli8q59DRXg62HqW6Ygb7m48c368ra9jc0MqG7U3Hz4HtXayuraSnL7jgkoj/jKVV1C0spSAW42h3H8+2HGXt8iq2725nzqxp7Gs+zLbGNtYsq2bp/FLufbiRZYvKqVtQyq8e2c3y08s5rXIWbZ3dnLmskjiJvB+cX9YsreLQsT6ezPpccPzcsyU8p5y9sobVSyvZWK/ceKQn2E/bdrdz5YWL2bWvk0153CdjdV6KxfP0TprZK4CXufv1ZnYh8H53f3k4bR7wS+A8oAT4A3Ceux9L115T06GsA+041s/Hb3+Ylo5uAG56xVnccd+2wecAVWUlvPqFKwEGpyXPl26Z97/pOXziG4+mfP3/fe73/Ps7n59y+qtfuJIv/vCplOtNNV/y8zvu2zb4/83XncNnvvP4sMu9+7XnsPfg4WG3J1W76ea97spVfP6OJ096DTjh9Xe8ej3fuXfrsPs7m7YTr2WKacn8ORn3eeJ5tvsuVSy3/NUFzJoWXKE40tPPLV99OO2+/NCbL+Cfbj95enIbNTVzaGo6RL6kizE5hpHMn8uy+d7miSZ5e2tq5kzga6qB8czZEOTtnXs7BnPHTa84i+WLytJ+thN58Ys/fIqbXnEWxYUFfOferYPT0i2T+Hxnk++H5tHkaenOBddduYrTqmbybMvRk/Jgptx4+mmzU+an97/pOTTuP5R2uS/+8Km07SbyY+P+4DhMnifTOSH5XJX8OLH/Tquayd6Dhwf3ear1Ll1Yyq5nOtPug0zv09BzYHF4JTvdugB6+we4475tvPu15/DZ/02f51M9zrQduZ4LEsdlqm3MJq9mazLk1V5ifOALD2T8rI3mPoFo57R0+zCbvJ3PPtEXA3cDuPtDBMk34XzgAXc/5u4dQD1w1mistLR0Go9vPTi44+ZWzKC5veuEHQnQ0tFNW2c3Xcf6aOnoPmG+TMtsqG+mcEjXjJaObp6qb+aGl6xmw/bmlMu1dHQxt2IGLR3ddPf00dqZuv3EfMnPCwtig69vaWhNuy3Jy+15tpP2Q93Dbk+i/a0NrRQWxDLOe6D1yOA6kl871NUz+PrcihnsbzmScvlM252u7bkVM5hbMWNwW1Itt31ve8r3JHlfFhbE0u67ofs8VSwb6pspKAi+tnxymPf4saTjL1Ub+ZYpxlQxRJ1/tJaVCWdccjbAtGmFbNvdRlP70cGcNbOkKGM+TeTF1bUVdB3rY3/LEQoLYsPmugOtR1hdW5FVvt+2u41jvX2RzgXN7V00tR09KQ9myo19fQNsbWhLOe2p+mb6BgbS5v3VtRU825a63QOtR3im6TCHu3tOWPdw54TEuWro48T+a24/Sm9ff9rtaW7vYveBQ2n3wXDvU3I+buvspm9gIO26DrYd5VBXD909fcytmMHWxsx5fujjnt7+weMu1f7L9VyQfIwNnT6VcmNBQYw/bXk2q9pqtPbJWJ6X8tlhuBToSHreb2ZF7t6XYtohoCxTYxUVM1P2CU5la0Pr4OMFNbNp3N+Zcr6d+zpYOr/0pPkyLeMNrcyvns3Btq6T1nn9S87gaz/ZnHK5hv2dg8t1dfex65nU7SfPl/y8YX8nq5dW4Q1tabclebn2Qz3sfKZj2O1JtO+Nbcyvnk0sxrDzJm97w/5Oli4oHXw907qibHfya7EY7HimI+1yBTFSvifJbS6omZ1236Xb58ntbdrRwssvXQ7Axp0tadtZvbTqhOMvWXIbEPz1my/pYhwaQ67z57psPrd5Ippk2ztuORugtaObnfuO56yyWdP5/RPPpJx3aF7sPtZH4/7OrHJdYpls8v3WxlZq55WmnJbuXLBjXztzK2ec1Gam9RzqOp6vT4qhoZUL185POW3nvg5WL63KmFcXVM/iaFffCevOZj8dbOtK+TjR5uGjvWnbGG4fZLv+xDbOq5qZ+Ty+IHiPMp0j023Xka7eweNuuFgg2rkgXczZ5NUoJnqe2VD/NJD5uBvtfRL1nJbrPsxnEd0JJEdVECbjVNPmAO2ZGmtrO5rVSktLp7GqtpJHtzwLwL6mw1xy9qLB58nqFpRREv6YLXm+TMtYbSX3/LHhpNdX1Vby6OYDJ6w7We38Un73+F4AZpQUsXRh6bDzJT+/9JxFbNrRzLmrT0u7Lb95bM/g8/I501i2qGzY7Um0f9WFS7jnoUaAYecd+trMkmL2Nx8GMu/vKNs99LU/O+90Ht2cerny0pLB9adbfl/TYa68cEmkfZ5s7bIqWlqCdZxZV8UjTx9I2c6mHc2ct3peyvUk2hgYiOf1K7iCgljaGJNjyHX+XNc1Gb52HE1DunOMczRZGZecDcGV6MqyEmIxeHRzkLM6jhwbNp8m8uL5a+azZH4p9z+xd9j8lVhmTV31sPlx1ZLKwR9PD5XuXLBsQTkAS+afmOsyrWfOjGmsWlKZMsetqq2kN/wh1lB1C8p4qr6JM5dXp93mgoIYM2cUnRBPNueEdI8vPWcRsRjMnll80jYm74NYLJZ2H2TzPiVvY0/fQNp11S0oY0ZJcB5/eNP+tOfIdNs1a0YxyxaWpT2/jORckDjGhhour0Yx0fNqQUGMdcureeTpAxmPu9HcJ1HPaRm6cwy/rpGFmtEDwIsAwv51G5OmPQI838xKzKwMWA1sGo2Vdnb2cM6quVSVlQBwsK2L6vIZg88TqspKqCgtYcb0IqrKSk6YL9My65ZX0z/kTa4qK+Gs5dV87adbWLeiOuVyVWUzONjWRVVZCSXTiqgsTd1+Yr7k5/0D8cHXV9dWpt2W5OVOP62U8jklw25Pov1VtZX0D8QzzjuvctYJf5EnXpszY9rg6wfbuphfNSvl8pm2O13bB9u6ONjWNbgtqZZbsag85XuSvC/7B+Jp993QfZ4qlnXLqxkYiDMwEGf9MO/xuUnHX6o28i1TjKliiDr/aC0rE8645GyAnp5+Vi6uoKZ85mDOOtrdlzGfJvLiloY2ZkwvYn7VLPoH4sPmunmVs9jS0JZVvl+5uILpxUWRzgXV5TOoqZh5Uh7MlBuLigpYVVuRctpZy6spKihIm/e3NLRxWkXqdudVzmJhzWxml0w7Yd3DnRMS56qhjxP7r7p8JsVFhWm3p7p8BovnzUm7D4Z7n5LzcUVpCUUFBWnXNbdiJnNmTKNkWhEH27pYtSRznh/6eFpx4eBxl2r/5XouSD7Ghk6fSrlxYCDOeatPy6q2Gq19MpbnpXz+sDDxS++zCMbYuYEgQde7+13hL73/mqCQ/xd3/0Gm9qL8SCUxOscTfjAYnaOumjXLqnh6ZzNP72zljLpKlpxWyn1/2s2qJZWsqq3kqfomdj3TyeXnL2bXvg7q97Rz+QWLadzfydM7W7ElFZyxtIrdBzpZPK+UzbuOj86xbkUwOoc3tHKg5QhXnL+EJ7eHv3iuq6JuYTAix/JF5YOP6xaUsXZZNVsaWtm0s5kzllZx1vJgdI6nd7ZgSyqYXzWLpvYuaspn0HaoO0heZdNpO9KDN7RR/0zwi/C1dVVs2tnCpnB0jrNX1rCloY0tu1q44oIlwfbsbeeK85fQsL9jcHvmV83i2dZgdI7ZJcHoHIlfqr/wvMU0Huhk865g3sToHHsPHubpXS0sX1jOysUVTCsuZOb0Qh7fdnx0jvUramju6ApH1mhjTV0VS+bP4VcP72bF4grOXFY1OHrJqtrgl+7dx/rZWN/MjnCbli4s42hXL3ubgjbOXlnDqtrwV847m1m+sByrrWDGtGIa9newYnEFT+9oZtOOlsHRWLY0hKNzhLEmRud40g+ypaGV1bWVrLe5eEMrT25rYu2yKtatCEbneHL78bbWLU/9i+wN4S+uE/uy4/Ax1iw9PjpHMJJI6jbyffUgOcZ0MYxk/lyWnehXTEbbJPxh4bjlbDg+Oseho73B6Bx72nnxxUspDkfn8MY2bElFMJpFexdr66p4qv746ByrllRypLuX+j3tnBaOxpG8zLMtR1gejs7xuAejc1xxfjAix+DoHM+EoxfVVnLm8mru+WMji+fNYeXiCjbvahlsb/3K46NzeOPxXHzm8mB0joZ9ndiSSrp6epNydRVr66o50t3Lll2t1D8TjM5xWtUsWtqD4nLO7GlsrD8+OseZy6r59Z9289x1CyDO4HJr66pZGo6kseL0cs4IR+fY8Uw4OkeYH7uP9dNx+Bi2+PjoHNt2B/GsXzGX1bUVx0eSCs9Vv3x4N8sWlbFkfim/fnQPtQtKB/df3aIyymZOY+OOFrbtaeOqC2uJx+ODcZ2xtIplC8uYPaOY3Qc6WRSOzrGt8fg+WLaonMNHe3m29Sg15UEf6eT9urWhlQ3bmwfzanNHUBj39A3Q29c/2NYZS6uoW1BKrCDG0a4+nm09ytplVdTvaWf2zGnsbz7Ctt1trK2rYsn8Un75SDA6x9IFpdz3yB6Wn142ODrH2rpgdI4ntx0/v5xRV0XZrGI2ZHkuSD73bA3PKevD89amHdHzarYmQ16tqZlDwzPtbKhvZvvuoLZq2BeM/pWPfQLRzmkj+WFh3oro0RY1IcPkHSe6sDAYjzNYFibiONGJv8RHa5zo0tIZdHYGf/FPhXGixyrxTaRxoidDsh9Nk62IHm255GwI9tuxYz0Tepzo5BydjbEeJ7qwsJDe3j4m8jjRM2ZMo6urZ0KPE11VNZuWlsMTepzoyZBXEzFO1HGiR1JEj3+FmUednT1Az3iHEVlNzRxaW7PvTzgR9fRkf4JJOHas74TnAwMntzEwEKe7u++k1xPTkpcZGIif1CYEibyvbyDtculeG259UaePhagxjCTmibC9cmro7Ew7ct6EMNFzdE3NHNrbu4afcZz09PQzY8Y0Dh+O+j4Pn18GBuL09fWkeG34daU6Z+RyLhju+VR18n7J/z7J977Xbb9FRERERCKaNN05REREREQmCl2JFhERERGJSEW0iIiIiEhEKqJFRERERCJSES0iIiIiEpGKaBERERGRiFREi4iIiIhEpCJaRERERCSiU/qOhZONmRUDtwO1wHTg4+5+17gGNQbMbC7wGHCFu28d73jyzczeD7wMmAbc5u5fHeeQ8iY8pr9BcEz3A285ld9jM7sA+KS7X2Zmy4GvE9wjeRPwdncfyLT8VGZmBcBtwDrgGHCju9ePb1QBM3sc6Ayf7gK+BNwK9AH3uvtHxymuYY83M/sI8OIw1ne5+yPjGOPZwE+B7eHkL7j7/41XjKnOucBmJtB+TBPjHibWfiwEvgwYwX67CehmYu3HVDEWM8L9qCvRE8vrgRZ3fz5wNfCf4xxP3oUJ4kvAxL1H7Sgys8uA5wLPAy4FTh/XgPLvRUCRuz8X+Bjwz+McT96Y2XuArwAl4UufAT4Yfp5jwMvHK7ZJ4lqgxN0vAt4HfHp8wwmYWQkQc/fLwn83AF8ErgMuBi4Ii8OxjmvY483MziHIMxcArwH+a5xjPBf4TNK+/L9xjjHVOXei7cdUMU60/fhSAHd/HvBBgjw/0fZjqhhHvB9VRE8sdwAfCh/HCP4KOtX9O8EJad94BzJGrgI2AncCPyH4K/hUtg0oCq8ylgK94xxPPu0AXpH0/Fzgd+HjXwCXj3lEk8vFwN0A7v4QcN74hjNoHTDTzO41s1+b2SXAdHff4e5x4B7G573N5ni7mOBKedzddxN8FmvGOcYXm9n9ZvZVM5szzjGmOudOtP2YLsYJsx/d/UfAX4dPlwDtTLD9mCHGEe1HFdETiLsfdvdD4Rv5fYK/lk5ZZnY90OTu94x3LGOomqA4eDXB10nfNrPY+IaUV4cJvobcSvBV2ufGNZo8cvcfcOIfCbGwyAI4BJSNfVSTSinQkfS838wmQpfDowR/7F9F8Jn9Wvhawri8t1keb0P36ZjGmiLGR4B/cPdLgJ3AR8YzxjTn3Am1H9PEOKH2Yxhnn5l9A/g88G0m2H5ME+OI96OK6AnGzE4HfgN8y92/M97x5NmbgSvM7LfAeuCbZjZvXCPKvxbgHnfvcXcn6Dc2lleGxtq7CbZ3JcEVvW+EX49PBcn9n+cQXPmQ9DoJ9lNCgbtPhG/jtgH/E16Z2kZwgq1Mmj5R3ttUx9vQfTresd7p7o8lHgNnM84xpjjnTrj9mCLGCbcfAdz9TcBKggsmM1LEMtFivHek+1FF9ARiZqcB9wLvdffbxzuefHP3S9z9Une/DHgSeKO7HxjfqPLuD8DVZhYzswXALILC+lTVxvG/6lsJfshROH7hjKknwj7wANcAvx/HWCaDBwj60GNmFxJ0e5oI3kzYPzv8zM4EjpjZsvBbpKuYGO9tquPtAeAqMysws8UEf5g0j1eAwD1mdn74+IUEPygftxjTnHMn1H5ME+NE249vCH8wD8G3NAPAnybYfkwV4w9Huh8nwldlctwHgArgQ2aW6AN1jbtPiR/dTQXu/tOwT+UjBH/Evt3d+8c5rHz6LHC7mf2eYDSSD7j7kXGOaaz8PfBlM5sGbCH4KlbSu5Pgm6kHCfp+3jDO8SR8Ffi6mf2B4Ff9byY4AX+b4A/Ce9394XGML+Gk483d+8PP3h8J8814Bgi8Dfi8mfUCB4C/dvfOcYwx1Tn374DPTaD9mCrGm4HPTqD9+EPga2Z2P8GFkncR7LuJdDyminEPIzweY/F4PNN0EREREREZQt05REREREQiUhEtIiIiIhKRimgRERERkYhURIuIiIiIRKQiWkREREQkIhXRIhGY2VfMbKLcjlhEZEows6+b2QfM7OcZ5ikzsx+NYVgyxWmcaJEI3P3G8Y5BRGSK2ufuL8owvYLg7rciY0LjRMukZ2ZFwBeAtcBpgBMMRn8n0Exwa+2bgf8m+MOxG7jB3beb2dXAxwgGX98FvMXdW8ysAfgWwd3IZhHcTfGx8BbltwC/A/4V+HOgD/iSu986FtsrInKqC+8G+WngJcA+ghvbfBW4xd1rzew64D1AP0Hufj3wPeBq4Gfu/ufjErhMKerOIaeC5wI97n4RsByYQXD7YANe7+6XA+8GPu3u5wGfBy40sxqCQvgqdz8buAf4ZFK7Le5+PvBFgrtGJXsV8DzgTOB84AYzm5evDRQRmWJeCZwNrAFeTZDbk30cuNLdzwW2AquAdxJcrVYBLWNC3Tlk0nP3+82sxczeTpBIVwCzgYPu3hDO9jPgv8Irzz8luAXzNcBi4DdmBsGVjtakpu8O/98EvGLIai8Fvufux4Bj6CtEEZHRdBnwQ3fvBZpS9IX+CfBA2Af6B+7+pJnVjm2IMtXpSrRMemb2MuDbwFHga8D9QCPQlZjH3b8PnAM8AryL4OpyIfAHd1/v7uuB5xBcYU7oDv+PA7Ehq+0dEkOtmc0anS0SEZny4pxYo/QlT3T3vyO4Wt0K/I+ZvX4MYxMBVETLqeFygqvCXwMOAJcQFMiDzOz/gPPd/UvAhwgK6oeBi8xsZTjbh4BPZbnO+4FXmFmxmc0kuGq9cMRbIiIiAL8CXm1m082sgqCvMxD8DsbMtgPN7v4J4JsEXT/60DfsMoZURMup4MvAa83sCeCHwEPAC4bM8y/AB8zsceDfgZvd/QDwZuB7ZraRoLD++2xW6O53Ag8AjwOPAre6+7bR2BgRkanO3X8M/JagO91dwOakaX3Ah4FfmdmfCC6cfAZ4FthtZr8Z84BlStLoHCIiIiIiEU2arz2amg6p2heRSammZs7QPvWnPOVsEZnMssnb6s4hIiIiIhJR3q5Em1kBcBuwjmAIsBvdvT5p+jXARwhGPXgMeLu768qFiIiIiEx4+bwSfS1QEt4A430Edx4CwMzmEIyC8BJ3vwBoAKrzGIuIiIiIyKjJZ5/oiwlvVuHuD5nZeUnTngtsBD5tZnXAV9y9KVNjFRUzKSoqzDSLiIiMkJldAHzS3S8b8vpLCUZE6ANud/cvj0N4IiITRj6L6FKgI+l5v5kVhUPTVBMMQbYeOAz83sz+mGmIsLa2o3kMVUQkf2pq5ox3CFkxs/cAbwCODHm9GPgswQ2JjhDcKe4ud3927KMUEZkY8tmdoxNIPnMUhAU0QAvwqLsfcPfDBDeuWJ/HWEREZHg7OPkW9wCrgXp3b3P3HuAPBGPziohMWfm8Ev0A8FKCG1lcSNB9I+FxYK2ZVQPtwIUEN8zIi7/71F35alomkFv/4WXjHYLIpObuPzCz2hSThn6zeAgoy9SWuuCJyHg7+PlfRV5m7jsuz3refBbRdwJXmNmDBCNw3GBmNxNczbjLzN4P3BPO+z1335THWEREJHdDv1mcQ3ABJC11wRORyaip6RCQXTe8vBXR7j4A3DTk5a1J078LfDdf6xcRkVGzBVhhZpUEv2O5BPj38Q1JRGR8TZo7FoqIyNgys+uA2e7+3+E3ifcQ/Jbmdnd/ZnyjExEZXyqiRURkkLs3EPxOBXf/TtLrPwF+Mk5hiYhMOLrtt4iIiIhIRCqiRUREREQiUhEtIiIiIhKR+kSLjIJ/+OkHxzsEybNPveTj4x2CiIhMILoSLSIiIiISkYpoEREREZGIVESLiIiIiESkIlpEREREJCIV0SIiIiIiEamIFhERERGJSEW0iIiIiEhEKqJFRERERCJSES0iIiIiElHe7lhoZgXAbcA64Bhwo7vXJ02/FbgYOBS+9HJ378hXPCIiIiIioyWft/2+Fihx94vM7ELg08DLk6afC1zl7s15jEFEREREZNTlszvHxcDdAO7+EHBeYkJ4lXoF8N9m9oCZvTmPcYiIiIiIjKp8XokuBZK7Z/SbWZG79wGzgM8DnwEKgd+Y2Z/c/al0jVVUzKSoqDCP4cpkV1MzZ7xDkFOYji8REUmWzyK6E0g+6xSEBTTAUeBWdz8KYGa/Jug7nbaIbms7mq845RTR1HRo+JlEcjSS40sFuIjIqSef3TkeAF4EEPaJ3pg0bSXwgJkVmlkxQdePx/MYi4iIiIjIqMnnleg7gSvM7EEgBtxgZjcD9e5+l5l9C3gI6AW+6e5P5zEWEREREZFRk7ci2t0HgJuGvLw1afqngE/la/0iIpK9LIYl/XvgOmAA+Bd3v3NcAhURmSB0sxUREYGkYUmB9xEMSwqAmZUDfwdcBFwJ/MfYhyciMrEMeyXazN4C/DNQFb4UA+LurqEyREQmmBHk7BOGJTWz85KmHQEaCUZWmkVwNVpEZErLpjvHB4AXqM+yiMikkGvOzjQsKcAeYDPBsKSfGK4xDUsqIuPtYA7LRBlNKZsi+qAKaBGRSSPXnJ1pWNJrgPnA0vD5PWb2gLs/kq4xDUsqIpNRYjjTbIrptEW0mb0xfNhoZj8GfgwkEiru/s0RRSkiIqNmFHL2A8BLge+lGJa0DegCjrl73MzagfJRCl1EZFLKdCX6BeH/R8J/z0+aFgdURIuITBwjzdnDDUt6OfCQmQ0AfwB+OarRi4hMMmmLaHe/IfHYzM529yfMrAw4191/PSbRiYhIVkaas7MYlvQjwEdGK14Rkclu2CHuzOwTwCfDpzOBD5vZLfkMSkREcqOcLSIyNrIZJ/qlBD8qwd33A5cDr8xnUCIikjPlbBGRMZBNEV0EzEh6Po2gf52IiEw8ytkiImMgmyHuvgQ8ZmY/CZ9fA/xX/kISEZERUM4WmYT+6Q/tkZf50MXlox6HZG/YK9Hu/lngDcB+YDfwene/Ld+BiYhIdMrZIiJjI5vbfv/A3V8JPJL02n3u/sK8RiYiIpEpZ4uIjI1MN1u5E1gHLDSznUOW2ZPvwEREJHvK2SIiYyvTleg3AZXArcA7k17vA57NZ1AiIhKZcraIyBjK1Cd6ubs3AJ8BliT9WwY8N/+hiYhIBMrZIiJjKNOV6LcBbwFuSTEtDvxZpobNrAC4jeDrxWPAje5en2KenwE/dvcvZh+2iIgMMaKcLSIi0WS67fdbwofvcfdHc2j7WqDE3S8yswuBTwMvHzLPx4GKHNoWEZEko5CzRUQkgmzGif6Yma0EfgP8FLjX3Y9msdzFwN0A7v6QmZ2XPNHMXgUMJOYREZFRkWvOFpmS/up3v8tpua9eeukoRyKTzbBFtLtfY2YlwAsIBu3/jJltc/erh1m0FOhIet5vZkXu3mdma4HrgFcBH84m0IqKmRQVFWYzq0xRNTVzxjsEOYVNluNrBDlbREQiyGac6BrgUuAy4PlAK7Api7Y7geSzToG794WP3wgsBH4N1AI9Ztbg7mmvSre16UKKZNbUdGi8Q5BT2EiOr7EswEeQs0VEJIJsunM8G/77D+Ayd2/Lsu0HgJcC3wv7RG9MTHD39yQem9ktwIFMBbSIiGQt15wtIjl66/1P57Tcly5ZM8qRyFjKpog24IUEXw3+xsw2A79x9y8Ps9ydwBVm9iAQA24ws5uBene/ayRBi4hIWrnmbBERiSCbPtHbge1hMXwFcBPwHCBjQnb3gXDeZFtTzHdLtsGKiEhmueZsERGJJps+0d8FnkdQAP8ceIm7e74DExGR6JSzZSq54Xe5fbH9tUtfNsqRyFSUTXeO7wFvcfdDAGZWmt+QRERkBJSzRUTGQKbbfif0Ah80s9lmtgXYaWZvz3NcIiKSG+VsEZExkM2V6A8DbwBeAzwCvB34LfBf+QtLRERylFPONrMC4DZgHXAMuNHd65OmXwN8hOCH4o8Bb3f3eB7iF5FxtO0X3ZGXWXlNyQnPj3z7SOQ2Zr1u1gnPB/5nV+Q2Cl6/NPIyI5HNlWjcfSvwYuAudz8MTMtrVCIikrMcc/a1QIm7XwS8D/h0YoKZzQE+RdC/+gKgAage5bBFRCaVbIroZ83s88B5wN1m9mlgd37DEhGRHOWasy8G7gZw94fC5ROeSzDW/6fN7PfAs+7eNLphi4hMLtl053gt8OfAre5+xMx2ArfkNSoREclVrjm7FOhIet5vZkXhnWarCcadXg8cBn5vZn90923pGquomElRUWGOmyAT3Yt+9KGclvv5tf80ypHkZjTuIjr6bbSPOIb//UH0v21f+8qaE55vI3p3jqFxHCF6d46hbTwbuYWT2zg4Cm1kks040YeAbyY9V19oEZEJagQ5uxNIPnsUhAU0QAvwqLsfADCz+wkK6rRFdFvb0QhRy1TR1HRovEMARieOidDGRIjhVG0jm2I6myvRIiJy6nsAeCnwPTO7kKD7RsLjwFozqya4XHYhunmLjND1v/9WTst9/flvGOVIRHKjIlpERADuBK4I73QYA24ws5uBene/y8zeD9wTzvs9d980XoGOl8fv+svIy5zzsv/LQyQiMhFkc8fCIuAqoJIgsQLg7t9Mu5CIiIyLXHO2uw8Q3CI82dak6d8Fvjt6kYqITG7ZXIn+DrAE2AIkxgSNk9TnTkREJoyJlbO//+PclnvVy0c3DhGRUZZNEX2Wu6/KeyQiIjIalLNFRMZANuNEbzGz+XmPRERERoNytojIGMjmSvRMwM1sExwfPNDd/yxvUYmISK6Us0VExkA2RfS/5D0KEREZLcrZKez//usiLzP/Vd/OQyQicqrI5mYrvzOza4AXhvP/xt2H/aWImRUAtwHrgGPAje5enzT97cD1BD94+Xd3/15OWyAiIoNyzdkiIhJNNkPcvQd4JfBtguGS/tHM1rj7cFc7rgVK3P2icOD+TwMvD9usBt4GnA2UAJvN7A53j6drTEREhjeCnC2TxDd/9arIy7zx8u/nIRKRqS2b7hyvBy5w9y4AM/sy8BjDf2V4MXA3gLs/ZGbnJSa4e7OZrXf3PjOrBbpVQIuIjIpcc7ZMIe/5w6sjL/NvF9+Rh0hEJq9siuiCRDIOdQN9WSxXCnQkPe83syJ37wMIC+i/BT4KfG64xioqZlJUVJjFamWqyuY+9yK5mkTHV645W0REIsimiL7PzH4AfD18/ibg11ks1wkkn3UKEgV0grv/p5n9N/ALM3uBu/8mXWNtbUezWKVMZU1Nh8Y7BDmFjeT4GuMCPNecLSIiEWRTRL+L4FawbyQYV/rXwJeyWO4B4KXA98I+0RsTE8zMgE8Q9NvrJfjh4UCUwEVEJKV3kVvOljFw98+i92e++sXqzywyEaUtos1snrsfAE4Hfhb+S1gA7B6m7TuBK8zsQYIft9xgZjcD9e5+l5ltAP5IMDrHL9z9dyPYDhGRKW0UcraIiESQ6Ur0V4CXAL8jKHQTYuHzukwNu/sAwdWQZFuTpn+UoD+0iIiM3IhytoiIRJO2iHb3l4QPz3X31uRp4YgaIiIyQShni4iMrUzdOU4nuILx83Dg/ljSMj8HVuU/PBERyYZytojI2MrUneOjwAsI+tLdn/R6H/DTfAYlIiKRKWeLiIyhTN053gxgZu9190+OXUgiIhKVcraIyNgqyGKe6/MdhIiIjJrrxzsAEZGpIJtxojeb2YeBh4HBu2C5+/3pFxERkXGinC0iMgayKaIrCfrZvSDptTjwZ3mJSERERkI5W0RkDAxbRLv7CwDMbA5Q6O7t+Q5KRERyo5wtIjI2hi2izawO+C6wDIiZWSPwF+6+Pd/BiYhINLnmbDMrAG4D1gHHgBvdvT7FPD8DfuzuX8xH/CIik0U2Pyz8EvBv7l7l7pXAJ4Av5zcsERHJUa45+1qgxN0vAt4HfDrFPB8HKkYrUBGRySybPtHV7v79xBN3/56ZfTCPMYmISO5yzdkXA3eHyzxkZuclTzSzVwEDiXmGU1Exk6KiQpqyj/sENTVzclwytf3jFIPaUBvZt9E+CjF0j7iNbaPQxhGOjLiNZyO3cHIbB0ehjUyyKaKPmdk57v44gJmdCxzNIS4REcm/XHN2KdCR9LzfzIrcvc/M1gLXAa8CPpxNEG1tIztNNDUdGtHyo2E0YlAbaiOfbUyEGE7VNrIpprMpot8F/MDMWgluI1sJ/GXu4YmISB69i9xydieQfNYocPe+8PEbgYXAr4FaoMfMGtw9q6vSIiKnomxG53jIzFYCKwkS8jZ378l7ZCIiEtkIcvYDwEuB75nZhcDGpDbfk3hsZrcAB1RAi8hUN+wPC81sMfB94CHgfuB2M6vJd2AiIhLdCHL2nUC3mT0IfBZ4t5ndbGYvy1+0IiKTVzbdOb4N/B/weoKi+83AN4AX5TEuERHJTU45290HgJuGvLw1xXy3jEqUIiKTXDZFdKm7/2fS88+a2fXDLTTcmKNm9m7gNeHTn7v7R7OOWkRE0skpZ4uISDTZjBP9mJm9PvHEzF4MPJHFcteSZszR8GYArwOeC1wIXGlmZ0WIW0REUss1Z4uISATZXIl+CXC9mf03wRihMwHM7I1A3N0L0yyXaczRPcDV7t4ftlVMLoMbiojIULnmbBERiSCb0Tnm5th22jFH3b0XaDazGPAp4Al335apscTA/SLpjPbNGUSSTZbjawQ5W0REIhi2iDazmcBHgBeG8/8a+JC7D3c7mkxjjmJmJcDtwCHgb4aLY6QD98upbyLcnEFOXSM5vsayAB9Bzp6w+u/498jLFL76/+UhEhGR47LpE/2fwCyCX3i/CZgGfDGL5R4g/DX40DFHwyvQPwY2uPtbE906RERkxHLN2SIiEkE2faLPdfd1Sc//1sw2Z7HcncAV4ZijMeAGM7sZqAcKgUuB6WZ2TTj/+939jxFiFxGRk+Was0VEJIJsiugCMyt393YAMysH+jIuQVZjjpZkGaOIiGQvp5wtIiLRZFNEfwZ4xMx+Ej5/GfCJ/IUkIiIjoJwtIjIGsimifwI8StD9ogB4hbtvzLyIiIiME+VsEZExkE0R/Xt3Xw1syncwIiIyYsrZIiJjIJsieoOZvQF4BOhKvOjuu/MWlYiI5Eo5W0RkDGRTRF8Q/ksWB+pGPxwRERkh5WwRkTGQzR0Ll45FICIiMnLK2SIiYyNtEW1mCwgG7V8B/IFgHOf2MYpLREQiUM4WERlbme5Y+DWCcZ3/gWBM58+OSUQiIpIL5WwRkTGUqTvHQne/CsDM7gOeHJOIREQkF8rZIiJjKNOV6J7EA3fvTX4uIiITjnK2iMgYylREDxXPWxQiIjLalLNFRPIoU3eONWa2M+n5wvB5DIi7u4ZLEhGZOJSzRUTGUKYieuWYRSEiIiM1opxtZgXAbcA64Bhwo7vXJ01/N/Ca8OnP3f2jI1mfiMhkl7aIdvfGsQxERERyNwo5+1qgxN0vMrMLgU8DLwcwszrgdQQ3cRkA/mBmd7r7UyNcp4jIpBWlT7SIiJy6LgbuBnD3h4DzkqbtAa529353jwPFQPfYhygiMnFkc9tvERE59ZUCHUnP+82syN37wtE+ms0sBnwKeMLdt2VqrKJiJkVFhTTlGExNzZzBxwdGuDzA/lFoIxdqQ21k30b7KMQQ/W/boW1sG4U2jnBkxG08G7mFk9s4OAptZJK3Inq4/nXhPDXAA8BZ7q6rGiIi46cTSD57FLh7X+KJmZUAtwOHgL8ZrrG2tqMjCqap6dC4Lq821MZkaGMixHCqtpFNMZ3P7hzXEvavA95H0L9ukJldBdwLzMtjDCIikp0HgBcBhH2iNyYmhFegfwxscPe3unv/+IQoIjJx5LM7xwn968zsvCHTB4DLgcfyGIOIiGTnTuAKM3uQYFi8G8zsZqAeKAQuBaab2TXh/O939z+OT6giIuMvn0V02v51AO7+SwAzy6qxRP86kXRGo3+aSDqn+vHl7gPATUNe3pr0uGQMwxERmfDyWURn7F8X1Uj718mpbzT6QomkM5Lj61QvwEVEpqJ89olO279ORERERGQyy+eV6LT969z9rjyuV0REREQkr/JWRGfRvy4xX22+YhARERERyQfdsVBEREREJCIV0SIiIiIiEamIFhERERGJSEW0iIiIiEhEKqJFRERERCJSES0iIiIiEpGKaBERERGRiFREi4iIiIhEpCJaRERERCQiFdEiIiIiIhGpiBYRERERiUhFtIiIiIhIRCqiRUREREQiUhEtIiIiIhKRimgRERERkYhURIuIiIiIRFSUr4bNrAC4DVgHHANudPf6pOlvAd4K9AEfd/ef5isWERHJTDlbRCSafF6JvhYocfeLgPcBn05MMLN5wDuB5wFXAZ8ws+l5jEVERDK7FuVsEZGs5bOIvhi4G8DdHwLOS5p2PvCAux9z9w6gHjgrj7GIiEhmytkiIhHE4vF4Xho2s68AP3D3X4TPdwN17t5nZq8HznT394bTvgl8091/lZdgREQkI+VsEZFo8nkluhOYk7wud+9LM20O0J7HWEREJDPlbBGRCPJZRD8AvAjAzC4ENiZNewR4vpmVmFkZsBrYlMdYREQkM+VsEZEI8tmdI/FL77OAGHADQYKud/e7wl96/zVBIf8v7v6DvAQiIiLDUs4WEYkmb0W0iIiIiMipSjdbERERERGJSEW0iIiIiEhEKqJFRERERCLK222/ZXwMd+tekdFgZhcAn3T3y8Y7FplcRjNHjeQ4NLNi4HagFphOcCvzuyK2UQh8GTAgDtzk7jmNWmJmc4HHgCvcfWsOyz9OMBQhwC53vyGHNt4PvAyYBtzm7l+NuPz1wPXh0xJgPTDP3dsjtFEMfIPgfekH3hJ1f4R30/waUEewT97u7tuzXHbwmDKz5cDXCd7bTWE7A1HbCZ//OfBqd78uhzjWA58n2B/HgDe6+7MR2zgD+G+CHw1vJ/jc9WVsIMV2hK9dB7wjvLtp1G05G/hpGAPAF9z9/yK2MZfgc1cBFBLsjx0R2/guMC+cVAs85O6vyWZ7kulK9KnnWtLculdkNJjZe4CvEJwkRaK6llHIUaNwHL4eaHH35wNXA/+ZQxsvBXD35wEfBP45l0DCwvFLQFeOy5cAMXe/LPyXSwF9GfBcglu7XwqcHrUNd/96IgaCPwjeGaWADr0IKHL35wIfI7d9+hbgsLtfCLyDLN/bFMfUZ4APhsdIDHh5Lu2Y2a3AJ8iy5koRx60ERetlwA+B9+bQxr8AHwiPVQiP3YhtEBbBf0WwP3LZlnOBzyQdq9kU0EPb+Dfg2+5+CcHnblXUNtz9NeH+/HOCMe/fnc32DKUi+tST6da9IqNhB/CK8Q5CJq3RylEjPQ7vAD4UPo4Bw16VG8rdf0Qw7B/AEnK/Ac2/A18E9uW4/Dpgppnda2a/Dsf5juoqgrHB7wR+QnC1MCdmdh6wxt3/O4fFtwFF4TcWpUBvDm2cAfwCwN2dYFzzbAw9ps4Ffhc+/gVweY7tPAi8LctlUy3/Gnd/MnxcBHTn0MYr3f1+M5tGcAW2I2obZlZFUIy/K4tl08VxLvBiM7vfzL5qZnPSLJepjecBi8zsV8DrgN/m0EbCR4HPu/v+LNo4iYroU08pJ344+s1M3XZk1ITjA+dyYhOBUcpRIz0O3f2wux8KT+LfJ7iilUs7fWb2DYKv278ddfmwC0STu9+Ty/pDRwkK8auAm4Bv57BPqwn+oHl1UhtZXW1M4QMExUkuDhN8vb6V4Cv7z+XQxpPAS8wsFv5BsTDsepNRimMq5u6JcYAPAWXZrHxoO+HV1qzHE06x/H4AM3su8LfAZ3Noo9/MlgBPE7zXG6K0Ee6/rwI3E+yLnLaF4MZN/xBeRd4JfCSHNmqBNne/HNhNFlfmU+WLsFvICwm67ORERfSpJ9Ote0VExtuEyVFmdjrwG+Bb7v6dXNtx9zcBK4Evm9msiIu/GbjCzH5L0If4m2Y2L+MSJ9sG/I+7x919G9ACzI/YRgtwj7v3hFdvu4GaiG1gZuWAuftvoi4bencYx0qCK+zfCLurRHE7wXH2e4Kv6x9z9/4cYknu/zyut7o3s78k+Lbixe7elEsb7t7o7ivCdj4TcfFzgRXAF4DvAmeY2X/kEMad7v5Y4jFwdg5ttACJ3y/8hNy/zXoV8J0cjw1ARfSpKNOte0VExtuEyFFmdhpwL/Bed789xzbeEP4YD4KrwQOcWHgNy90vcfdLw/6ZTxL8SOpAxFDeTNi33MwWEFztj/r19B+Aq8OrtwuAWQTFSlSXAPflsFxCG8e/qWgFigl+PBbFc4D73P1igm47O3OM5YmwrzjANQRF+Zgzs9cTXIG+zN1z2hYzu8vMVoRPDxH9OH3E3deEx+lrgM3u/q4cQrnHzM4PH7+QoO98VH8gzCEEx9vTObQBQfecX+S4LKDROU5FdxJc1XiQ47fuFRGZKCZKjvoAwa/7P2Rmib7R17h7lB/3/RD4mpndT1DsvSvi8qPlq8DXzewPBN0G3hz16r67/9TMLiH4ur2AYCSKXK7QGbkXrRB0VbjdzH5PMErIB9z9SMQ2tgP/ZGb/SHD1+K9yjOXvCb5dmAZsIej2M6bCbhSfI+i28EMzA/iduw/bDWKIfyU4RnoI/uC7cVQDzd7bgM+bWS9wgOO/KYji74GvmNnbCP7gymrEkxRGeqzqtt8iIiIiIlGpO4eIiIiISEQqokVEREREIlIRLSIiIiISkYpoEREREZGIVESLiIiIiESkIlpEREQmBTO7xcxuGe84REBFtIiIiIhIZLrZipxSzKyI4Laka4HTACe4u9J3gMStdD/q7neZ2c3Amwju3PSIu7/VzAqA/yC4k1Kc4HbAnzSzGMFg9X8O9AFfcvdbx27LREROfWb2PuAvCO5SeA/wXuD/EdyUo5ngjoaPhPPG3T0WPr6e4I5+15vZ5QR3cCwAGoHr3L1zjDdFpgBdiZZTzXOBHne/CFgOzACuBRrc/Vzg9cDzw2L7/cB5wLnAgJktBG4CTgfOAs4HXmlmLwZeBTwPODN8/QYzm4eIiIwKM7uaIB8/BzgbWAj8I8Ftzc8muE3zomHamA58G3iTu58JPEVwsURk1OlKtJxS3P1+M2sxs7cDq4AVQC1wbVgk/wz4J3fvC287/CjwY+C/3P0ZM/sz4Ovh7W6Pmtm3Ca5KTwO+5+7HgGPA+rHeNhGRU9zlwAXAY+HzGcArgNvc/TCAmd1BcJU6nTOBZ9z9SQB3/0DeopUpT1ei5ZRiZi8juApxFPgacD/B13mrwtefDzwSds+4FngbEAPuNrNLOfkzESP4Y7N3yHpqzWxW/rZERGTKKQT+w93Xu/t6goL6XzkxL/clLxDmcoDi8P+hubrMzDJevRbJlYpoOdVcTnDF+GvAAeASoIygH/QdwN8Ac4FqYAuw0d0/DNxL0IXj18CbzKzQzGYCrwN+Q1CMv8LMisPX7yb4qlFEREbHr4E3mNnssMvdj4AO4CVhMVxC8LuUhGZgTVhIvyx8zYEaMzsjfP4egm56IqNO3TnkVPNl4Dtm9mqCbhcPAUsAM7ONBFcpbnH3JjP7EvComR0FdgNfB7qBlcAGgisb/+PudxI0cB7wOMEfn7e6+7Yx3TIRkVOYu//EzNYBDxNclb4buJUgbz9K8KPCxqRF3gf8lOCCyR+AanfvNrPXA980s2nADuANY7cVMpXE4vH4eMcgIiIiIjKpqDuHiIiIiEhEKqJFRERERCJSES0iIiIiEpGKaBERERGRiFREi4iIiIhEpCJaRERERCQiFdEiIiIiIhH9f7I3c3D8C1teAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2, figsize=(12, 6))\n",
    "fig.suptitle(\"Target variable plotted against various predictors\")\n",
    "sns.scatterplot(data=wells, x=\"arsenic\", y=\"switch\", ax=ax[0][0])\n",
    "sns.scatterplot(data=wells, x=\"dist\", y=\"switch\", ax=ax[0][1])\n",
    "sns.barplot(\n",
    "    data=wells.groupby(\"assoc\")[\"switch\"].mean().reset_index(),\n",
    "    x=\"assoc\",\n",
    "    y=\"switch\",\n",
    "    ax=ax[1][0],\n",
    ")\n",
    "ax[1][0].set_ylabel(\"Proportion switch\")\n",
    "sns.barplot(\n",
    "    data=wells.groupby(\"educ\")[\"switch\"].mean().reset_index(),\n",
    "    x=\"educ\",\n",
    "    y=\"switch\",\n",
    "    ax=ax[1][1],\n",
    ")\n",
    "ax[1][1].set_ylabel(\"Proportion switch\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c9daff",
   "metadata": {
    "papermill": {
     "duration": 0.046834,
     "end_time": "2021-09-18T10:52:02.167845",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.121011",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Next, we'll choose 200 observations to be part of our train set, and 1500 to be part of our test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e6b41da0",
   "metadata": {
    "papermill": {
     "duration": 0.058671,
     "end_time": "2021-09-18T10:52:02.274078",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.215407",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(1)\n",
    "train_id = wells.sample(n=200).index\n",
    "test_id = wells.loc[~wells.index.isin(train_id)].sample(n=1500).index\n",
    "y_train = wells.loc[train_id, \"switch\"].to_numpy()\n",
    "y_test = wells.loc[test_id, \"switch\"].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c56e27",
   "metadata": {
    "papermill": {
     "duration": 0.047031,
     "end_time": "2021-09-18T10:52:02.368998",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.321967",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 2. Prepare 6 different candidate models <a class=\"anchor\" id=\"2\"></a>\n",
    "\n",
    "### 2.1 Feature Engineering <a class=\"anchor\" id=\"2.1\"></a>\n",
    "\n",
    "First, let's add a few new columns:\n",
    "- `edu0`: whether `educ` is `0`,\n",
    "- `edu1`: whether `educ` is between `1` and `5`,\n",
    "- `edu2`: whether `educ` is between `6` and `11`,\n",
    "- `edu3`: whether `educ` is between `12` and `17`,\n",
    "- `logarsenic`: natural logarithm of `arsenic`,\n",
    "- `assoc_half`: half of `assoc`,\n",
    "- `as_square`: natural logarithm of `arsenic`, squared,\n",
    "- `as_third`: natural logarithm of `arsenic`, cubed,\n",
    "- `dist100`: `dist` divided by `100`,\n",
    " - `intercept`: just a columns of `1`s.\n",
    "\n",
    "We're going to start by fitting 6 different models to our train set:\n",
    "\n",
    "- logistic regression using `intercept`, `arsenic`, `assoc`, `edu1`, `edu2`, and `edu3`;\n",
    "- same as above, but with `logarsenic` instead of `arsenic`;\n",
    "- same as the first one, but with square and cubic features as well;\n",
    "- same as the first one, but with spline features derived from `logarsenic` as well;\n",
    "- same as the first one, but with spline features derived from `dist100` as well;\n",
    "- same as the first one, but with `educ` instead of the binary `edu` variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fa79c0ee-54b9-458d-9f97-c9e91ae83e7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "wells[\"edu0\"] = wells[\"educ\"].isin(np.arange(0, 1)).astype(int)\n",
    "wells[\"edu1\"] = wells[\"educ\"].isin(np.arange(1, 6)).astype(int)\n",
    "wells[\"edu2\"] = wells[\"educ\"].isin(np.arange(6, 12)).astype(int)\n",
    "wells[\"edu3\"] = wells[\"educ\"].isin(np.arange(12, 18)).astype(int)\n",
    "wells[\"logarsenic\"] = np.log(wells[\"arsenic\"])\n",
    "wells[\"assoc_half\"] = wells[\"assoc\"] / 2.0\n",
    "wells[\"as_square\"] = wells[\"logarsenic\"] ** 2\n",
    "wells[\"as_third\"] = wells[\"logarsenic\"] ** 3\n",
    "wells[\"dist100\"] = wells[\"dist\"] / 100.0\n",
    "wells[\"intercept\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6726d0fa",
   "metadata": {
    "papermill": {
     "duration": 0.062523,
     "end_time": "2021-09-18T10:52:02.478421",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.415898",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def bs(x, knots, degree):\n",
    "    \"\"\"\n",
    "    Generate the B-spline basis matrix for a polynomial spline.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x\n",
    "        predictor variable.\n",
    "    knots\n",
    "        locations of internal breakpoints (not padded).\n",
    "    degree\n",
    "        degree of the piecewise polynomial.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    pd.DataFrame\n",
    "        Spline basis matrix.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    This mirrors ``bs`` from splines package in R.\n",
    "    \"\"\"\n",
    "    padded_knots = np.hstack(\n",
    "        [[x.min()] * (degree + 1), knots, [x.max()] * (degree + 1)]\n",
    "    )\n",
    "    return pd.DataFrame(\n",
    "        BSpline(padded_knots, np.eye(len(padded_knots) - degree - 1), degree)(x)[:, 1:],\n",
    "        index=x.index,\n",
    "    )\n",
    "\n",
    "\n",
    "knots = np.quantile(wells.loc[train_id, \"logarsenic\"], np.linspace(0.1, 0.9, num=10))\n",
    "spline_arsenic = bs(wells[\"logarsenic\"], knots=knots, degree=3)\n",
    "knots = np.quantile(wells.loc[train_id, \"dist100\"], np.linspace(0.1, 0.9, num=10))\n",
    "spline_dist = bs(wells[\"dist100\"], knots=knots, degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "064a3de6",
   "metadata": {
    "papermill": {
     "duration": 0.081958,
     "end_time": "2021-09-18T10:52:02.608879",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.526921",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "features_0 = [\"intercept\", \"dist100\", \"arsenic\", \"assoc\", \"edu1\", \"edu2\", \"edu3\"]\n",
    "features_1 = [\"intercept\", \"dist100\", \"logarsenic\", \"assoc\", \"edu1\", \"edu2\", \"edu3\"]\n",
    "features_2 = [\n",
    "    \"intercept\",\n",
    "    \"dist100\",\n",
    "    \"arsenic\",\n",
    "    \"as_third\",\n",
    "    \"as_square\",\n",
    "    \"assoc\",\n",
    "    \"edu1\",\n",
    "    \"edu2\",\n",
    "    \"edu3\",\n",
    "]\n",
    "features_3 = [\"intercept\", \"dist100\", \"assoc\", \"edu1\", \"edu2\", \"edu3\"]\n",
    "features_4 = [\"intercept\", \"logarsenic\", \"assoc\", \"edu1\", \"edu2\", \"edu3\"]\n",
    "features_5 = [\"intercept\", \"dist100\", \"logarsenic\", \"assoc\", \"educ\"]\n",
    "\n",
    "X0 = wells.loc[train_id, features_0].to_numpy()\n",
    "X1 = wells.loc[train_id, features_1].to_numpy()\n",
    "X2 = wells.loc[train_id, features_2].to_numpy()\n",
    "X3 = (\n",
    "    pd.concat([wells.loc[:, features_3], spline_arsenic], axis=1)\n",
    "    .loc[train_id]\n",
    "    .to_numpy()\n",
    ")\n",
    "X4 = pd.concat([wells.loc[:, features_4], spline_dist], axis=1).loc[train_id].to_numpy()\n",
    "X5 = wells.loc[train_id, features_5].to_numpy()\n",
    "\n",
    "X0_test = wells.loc[test_id, features_0].to_numpy()\n",
    "X1_test = wells.loc[test_id, features_1].to_numpy()\n",
    "X2_test = wells.loc[test_id, features_2].to_numpy()\n",
    "X3_test = (\n",
    "    pd.concat([wells.loc[:, features_3], spline_arsenic], axis=1)\n",
    "    .loc[test_id]\n",
    "    .to_numpy()\n",
    ")\n",
    "X4_test = (\n",
    "    pd.concat([wells.loc[:, features_4], spline_dist], axis=1).loc[test_id].to_numpy()\n",
    ")\n",
    "X5_test = wells.loc[test_id, features_5].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64fa1b43",
   "metadata": {
    "papermill": {
     "duration": 0.055757,
     "end_time": "2021-09-18T10:52:02.713347",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.657590",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_x_list = [X0, X1, X2, X3, X4, X5]\n",
    "test_x_list = [X0_test, X1_test, X2_test, X3_test, X4_test, X5_test]\n",
    "K = len(train_x_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d1a65d",
   "metadata": {
    "papermill": {
     "duration": 0.049466,
     "end_time": "2021-09-18T10:52:02.811950",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.762484",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.2 Training <a class=\"anchor\" id=\"2.2\"></a>\n",
    "\n",
    "Each model will be trained in the same way - with a Bernoulli likelihood and a logit link function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c070567f",
   "metadata": {
    "papermill": {
     "duration": 0.056796,
     "end_time": "2021-09-18T10:52:02.917713",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.860917",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def logistic(x, y=None):\n",
    "    beta = numpyro.sample(\"beta\", dist.Normal(0, 3).expand([x.shape[1]]))\n",
    "    logits = numpyro.deterministic(\"logits\", jnp.matmul(x, beta))\n",
    "\n",
    "    numpyro.sample(\n",
    "        \"obs\",\n",
    "        dist.Bernoulli(logits=logits),\n",
    "        obs=y,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b29ed6c2",
   "metadata": {
    "papermill": {
     "duration": 820.388941,
     "end_time": "2021-09-18T11:05:43.355092",
     "exception": false,
     "start_time": "2021-09-18T10:52:02.966151",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fit_list = []\n",
    "for k in range(K):\n",
    "    sampler = numpyro.infer.NUTS(logistic)\n",
    "    mcmc = numpyro.infer.MCMC(\n",
    "        sampler, num_chains=4, num_samples=1000, num_warmup=1000, progress_bar=False\n",
    "    )\n",
    "    rng_key = jax.random.fold_in(jax.random.PRNGKey(13), k)\n",
    "    mcmc.run(rng_key, x=train_x_list[k], y=y_train)\n",
    "    fit_list.append(mcmc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2ac5012",
   "metadata": {
    "papermill": {
     "duration": 0.051074,
     "end_time": "2021-09-18T11:05:43.479751",
     "exception": false,
     "start_time": "2021-09-18T11:05:43.428677",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 2.3 Estimate leave-one-out cross-validated score for each training point <a class=\"anchor\" id=\"2.3\"></a>\n",
    "\n",
    "Rather than refitting each model 100 times, we will estimate the leave-one-out cross-validated score using [LOO](https://arxiv.org/abs/2001.00980)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0dfe6166",
   "metadata": {
    "papermill": {
     "duration": 14.787853,
     "end_time": "2021-09-18T11:05:58.318434",
     "exception": false,
     "start_time": "2021-09-18T11:05:43.530581",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def find_point_wise_loo_score(fit):\n",
    "    return az.loo(az.from_numpyro(fit), pointwise=True, scale=\"log\").loo_i.values\n",
    "\n",
    "\n",
    "lpd_point = np.vstack([find_point_wise_loo_score(fit) for fit in fit_list]).T\n",
    "exp_lpd_point = np.exp(lpd_point)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f7a74a",
   "metadata": {
    "papermill": {
     "duration": 0.051972,
     "end_time": "2021-09-18T11:05:58.422802",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.370830",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 3. Bayesian Hierarchical Stacking <a class=\"anchor\" id=\"3\"></a>\n",
    "\n",
    "### 3.1 Prepare stacking datasets <a class=\"anchor\" id=\"3.1\"></a>\n",
    "\n",
    "To determine how the stacking weights should vary across training and test sets, we will need to create \"stacking datasets\" which include all the features which we want the stacking weights to depend on. How should such features be included? For discrete features, this is easy, we just one-hot-encode them. But for continuous features, we need a trick. In Equation (16), the authors recommend the following: if you have a continuous feature `f`, then replace it with the following two features:\n",
    "\n",
    "- `f_l`: `f` minus the median of `f`, clipped above at 0;\n",
    "- `f_r`: `f` minus the median of `f`, clipped below at 0;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8450ac11",
   "metadata": {
    "papermill": {
     "duration": 0.078407,
     "end_time": "2021-09-18T11:05:58.566113",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.487706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dist100_median = wells.loc[wells.index[train_id], \"dist100\"].median()\n",
    "logarsenic_median = wells.loc[wells.index[train_id], \"logarsenic\"].median()\n",
    "wells[\"dist100_l\"] = (wells[\"dist100\"] - dist100_median).clip(upper=0)\n",
    "wells[\"dist100_r\"] = (wells[\"dist100\"] - dist100_median).clip(lower=0)\n",
    "wells[\"logarsenic_l\"] = (wells[\"logarsenic\"] - logarsenic_median).clip(upper=0)\n",
    "wells[\"logarsenic_r\"] = (wells[\"logarsenic\"] - logarsenic_median).clip(lower=0)\n",
    "\n",
    "stacking_features = [\n",
    "    \"edu0\",\n",
    "    \"edu1\",\n",
    "    \"edu2\",\n",
    "    \"edu3\",\n",
    "    \"assoc_half\",\n",
    "    \"dist100_l\",\n",
    "    \"dist100_r\",\n",
    "    \"logarsenic_l\",\n",
    "    \"logarsenic_r\",\n",
    "]\n",
    "X_stacking_train = wells.loc[train_id, stacking_features].to_numpy()\n",
    "X_stacking_test = wells.loc[test_id, stacking_features].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb323c68",
   "metadata": {
    "papermill": {
     "duration": 0.052318,
     "end_time": "2021-09-18T11:05:58.671602",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.619284",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 3.2 Define stacking model <a class=\"anchor\" id=\"3.2\"></a>\n",
    "\n",
    "What we seek to find is a matrix of weights $W$ with which to multiply the models' predictions. Let's define a matrix $Pred$ such that $Pred_{i,k}$ represents the prediction made for point $i$ by model $k$. Then the final prediction for point $i$ will then be:\n",
    "\n",
    "$$ \\sum_k W_{i, k}Pred_{i,k} $$\n",
    "\n",
    "Such a matrix $W$ would be required to have each column sum to $1$. Hence, we calculate each row $W_i$ of $W$ as:\n",
    "\n",
    "$$ W_i = \\text{softmax}(X\\_\\text{stacking}_i \\cdot \\beta), $$\n",
    "\n",
    "where $\\beta$ is a matrix whose values we seek to determine. For the discrete features, $\\beta$ is given a hierarchical structure over the possible inputs. Continuous features, on the other hand, get no hierarchical structure in this case study and just vary according to the input values.\n",
    "\n",
    "Notice how, for the discrete features, a [non-centered parametrisation is used](https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/). Also note that we only need to estimate `K-1` columns of $\\beta$, because the weights `W_{i, k}` will have to sum to `1` for each `i`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2203a8c",
   "metadata": {
    "papermill": {
     "duration": 0.075301,
     "end_time": "2021-09-18T11:05:58.799743",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.724442",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def stacking(\n",
    "    X,\n",
    "    d_discrete,\n",
    "    X_test,\n",
    "    lpd_point,\n",
    "    tau_mu,\n",
    "    tau_sigma,\n",
    "    *,\n",
    "    test,\n",
    "):\n",
    "    \"\"\"\n",
    "    Get weights with which to stack candidate models' predictions.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    X\n",
    "        Training stacking matrix: features on which stacking weights should depend, for the\n",
    "        training set.\n",
    "    d_discrete\n",
    "        Number of discrete features in `X` and `X_test`. The first `d_discrete` features\n",
    "        from these matrices should be the discrete ones, with the continuous ones coming\n",
    "        after them.\n",
    "    X_test\n",
    "        Test stacking matrix: features on which stacking weights should depend, for the\n",
    "        testing set.\n",
    "    lpd_point\n",
    "        LOO score evaluated at each point in the training set, for each candidate model.\n",
    "    tau_mu\n",
    "        Hyperprior for mean of `beta`, for discrete features.\n",
    "    tau_sigma\n",
    "        Hyperprior for standard deviation of `beta`, for continuous features.\n",
    "    test\n",
    "        Whether to calculate stacking weights for test set.\n",
    "\n",
    "    Notes\n",
    "    -----\n",
    "    Naming of variables mirrors what's used in the original paper.\n",
    "    \"\"\"\n",
    "    N = X.shape[0]\n",
    "    d = X.shape[1]\n",
    "    N_test = X_test.shape[0]\n",
    "    K = lpd_point.shape[1]  # number of candidate models\n",
    "\n",
    "    with numpyro.plate(\"Candidate models\", K - 1, dim=-2):\n",
    "        # mean effect of discrete features on stacking weights\n",
    "        mu = numpyro.sample(\"mu\", dist.Normal(0, tau_mu))\n",
    "        # standard deviation effect of discrete features on stacking weights\n",
    "        sigma = numpyro.sample(\"sigma\", dist.HalfNormal(scale=tau_sigma))\n",
    "        with numpyro.plate(\"Discrete features\", d_discrete, dim=-1):\n",
    "            # effect of discrete features on stacking weights\n",
    "            tau = numpyro.sample(\"tau\", dist.Normal(0, 1))\n",
    "        with numpyro.plate(\"Continuous features\", d - d_discrete, dim=-1):\n",
    "            # effect of continuous features on stacking weights\n",
    "            beta_con = numpyro.sample(\"beta_con\", dist.Normal(0, 1))\n",
    "\n",
    "    # effects of features on stacking weights\n",
    "    beta = numpyro.deterministic(\n",
    "        \"beta\", jnp.hstack([(sigma.squeeze() * tau.T + mu.squeeze()).T, beta_con])\n",
    "    )\n",
    "    assert beta.shape == (K - 1, d)\n",
    "\n",
    "    # stacking weights (in unconstrained space)\n",
    "    f = jnp.hstack([X @ beta.T, jnp.zeros((N, 1))])\n",
    "    assert f.shape == (N, K)\n",
    "\n",
    "    # log probability of LOO training scores weighted by stacking weights.\n",
    "    log_w = jax.nn.log_softmax(f, axis=1)\n",
    "\n",
    "    # stacking weights (constrained to sum to 1)\n",
    "    numpyro.deterministic(\"w\", jnp.exp(log_w))\n",
    "    logp = jax.nn.logsumexp(lpd_point + log_w, axis=1)\n",
    "    numpyro.factor(\"logp\", jnp.sum(logp))\n",
    "\n",
    "    if test:\n",
    "        # test set stacking weights (in unconstrained space)\n",
    "        f_test = jnp.hstack([X_test @ beta.T, jnp.zeros((N_test, 1))])\n",
    "        # test set stacking weights (constrained to sum to 1)\n",
    "        numpyro.deterministic(\"w_test\", jax.nn.softmax(f_test, axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9827977d",
   "metadata": {
    "papermill": {
     "duration": 296.084187,
     "end_time": "2021-09-18T11:10:54.936288",
     "exception": false,
     "start_time": "2021-09-18T11:05:58.852101",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "sampler = numpyro.infer.NUTS(stacking)\n",
    "mcmc = numpyro.infer.MCMC(\n",
    "    sampler, num_chains=4, num_samples=1000, num_warmup=1000, progress_bar=False\n",
    ")\n",
    "mcmc.run(\n",
    "    jax.random.PRNGKey(17),\n",
    "    X=X_stacking_train,\n",
    "    d_discrete=4,\n",
    "    X_test=X_stacking_test,\n",
    "    lpd_point=lpd_point,\n",
    "    tau_mu=1.0,\n",
    "    tau_sigma=0.5,\n",
    "    test=True,\n",
    ")\n",
    "trace = mcmc.get_samples()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ede764",
   "metadata": {
    "papermill": {
     "duration": 0.052553,
     "end_time": "2021-09-18T11:10:55.042375",
     "exception": false,
     "start_time": "2021-09-18T11:10:54.989822",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can now extract the weights with which to weight the different models from the posterior, and then visualise how they vary across the training set.\n",
    "\n",
    "Let's compare them with what the weights would've been if we'd just used fixed stacking weights (computed using ArviZ - see [their docs](https://arviz-devs.github.io/arviz/api/generated/arviz.compare.html) for details)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "812117cb",
   "metadata": {
    "papermill": {
     "duration": 2.523295,
     "end_time": "2021-09-18T11:10:57.979955",
     "exception": false,
     "start_time": "2021-09-18T11:10:55.456660",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(16, 6), sharey=True)\n",
    "training_stacking_weights = trace[\"w\"].mean(axis=0)\n",
    "sns.scatterplot(data=pd.DataFrame(training_stacking_weights), ax=ax[0])\n",
    "fixed_weights = (\n",
    "    az.compare({idx: fit for idx, fit in enumerate(fit_list)}, method=\"stacking\")\n",
    "    .sort_index()[\"weight\"]\n",
    "    .to_numpy()\n",
    ")\n",
    "fixed_weights_df = pd.DataFrame(\n",
    "    np.repeat(\n",
    "        fixed_weights[jnp.newaxis, :],\n",
    "        len(X_stacking_train),\n",
    "        axis=0,\n",
    "    )\n",
    ")\n",
    "sns.scatterplot(data=fixed_weights_df, ax=ax[1])\n",
    "ax[0].set_title(\"Training weights from Bayesian Hierarchical stacking\")\n",
    "ax[1].set_title(\"Fixed weights stacking\")\n",
    "ax[0].set_xlabel(\"Index\")\n",
    "ax[1].set_xlabel(\"Index\")\n",
    "ax[0].legend(ncol=6, loc=\"upper center\")\n",
    "fig.suptitle(\n",
    "    \"Bayesian Hierarchical Stacking weights can vary according to the input\",\n",
    "    fontsize=18,\n",
    ")\n",
    "fig.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c60e0c01",
   "metadata": {
    "papermill": {
     "duration": 0.065143,
     "end_time": "2021-09-18T11:10:58.110931",
     "exception": false,
     "start_time": "2021-09-18T11:10:58.045788",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## 4. Evaluate on test set <a class=\"anchor\" id=\"4\"></a>\n",
    "\n",
    "### 4.1 Stack predictions <a class=\"anchor\" id=\"4.1\"></a>\n",
    "\n",
    "Now, for each model, let's evaluate the log predictive density for each point in the test set. Once we have predictions for each model, we need to think about how to combine them, such that for each test point, we get a single prediction.\n",
    "\n",
    "We decided we'd do this in three ways:\n",
    "- Bayesian Hierarchical Stacking (`bhs_pred`);\n",
    "- choosing the model with the best training set LOO score (`model_selection_preds`);\n",
    "- fixed-weights stacking (`fixed_weights_preds`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce86bd9e-b2c6-4947-9675-92c925b6088d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for each candidate model, extract the posterior predictive logits\n",
    "train_preds = []\n",
    "for k in range(K):\n",
    "    predictive = numpyro.infer.Predictive(logistic, fit_list[k].get_samples())\n",
    "    rng_key = jax.random.fold_in(jax.random.PRNGKey(19), k)\n",
    "    train_pred = predictive(rng_key, x=train_x_list[k])[\"logits\"]\n",
    "    train_preds.append(train_pred.mean(axis=0))\n",
    "# reshape, so we have (N, K)\n",
    "train_preds = np.vstack(train_preds).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5b686b7c",
   "metadata": {
    "papermill": {
     "duration": 0.54285,
     "end_time": "2021-09-18T11:10:59.694998",
     "exception": false,
     "start_time": "2021-09-18T11:10:59.152148",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# same as previous cell, but for test set\n",
    "test_preds = []\n",
    "for k in range(K):\n",
    "    predictive = numpyro.infer.Predictive(logistic, fit_list[k].get_samples())\n",
    "    rng_key = jax.random.fold_in(jax.random.PRNGKey(20), k)\n",
    "    test_pred = predictive(rng_key, x=test_x_list[k])[\"logits\"]\n",
    "    test_preds.append(test_pred.mean(axis=0))\n",
    "test_preds = np.vstack(test_preds).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "436f8789",
   "metadata": {
    "papermill": {
     "duration": 0.145066,
     "end_time": "2021-09-18T11:11:00.042707",
     "exception": false,
     "start_time": "2021-09-18T11:10:59.897641",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get the stacking weights for the test set\n",
    "test_stacking_weights = trace[\"w_test\"].mean(axis=0)\n",
    "# get predictions using the stacking weights\n",
    "bhs_predictions = (test_stacking_weights * test_preds).sum(axis=1)\n",
    "# get predictions using only the model with the best LOO score\n",
    "model_selection_preds = test_preds[:, lpd_point.sum(axis=0).argmax()]\n",
    "# get predictions using fixed stacking weights, dependent on the LOO score\n",
    "fixed_weights_preds = (fixed_weights * test_preds).sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76233762",
   "metadata": {
    "papermill": {
     "duration": 0.064289,
     "end_time": "2021-09-18T11:11:00.170538",
     "exception": false,
     "start_time": "2021-09-18T11:11:00.106249",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 4.2 Compare methods <a class=\"anchor\" id=\"4.2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2d889c2",
   "metadata": {
    "papermill": {
     "duration": 0.06178,
     "end_time": "2021-09-18T11:11:00.293209",
     "exception": false,
     "start_time": "2021-09-18T11:11:00.231429",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's compare the negative log predictive density scores on the test set (note - lower is better):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "33e15689",
   "metadata": {
    "papermill": {
     "duration": 0.463508,
     "end_time": "2021-09-18T11:11:00.819086",
     "exception": false,
     "start_time": "2021-09-18T11:11:00.355578",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9qP3YZhIRXwYeyswvtZbNzO9GxLmUrl3HR8Sq2Tbj12y4n3KcK3R5bvkhbGc5Zm7taa3bOpfuAiZ2eY8uTmk969bSNJC7KC1oM6l/QxahDP6f42q3ydcDj2SZFfC8+vi7KeMM96SMWezPMtSbMVWr/lutM3cxiPqKMtEKmXkpZaINokzscRll5rnZCT0DuYvh/a0Z7vrSsxzTI2lcqX3eNwJurN3IXlCfuq1juc0pF0udN29+SenmsQcluJzReiIz/0VpedklIl7Stq0JwA8osyS1LjSvoa11pt4Bb/VX7+su8BLA3R2BZ2lm3KUcLzeaWjPjfar9wYh4B2UWvIHCyxLAo7R1Lawta/vUXwc6ztb4kn06Ht+FMnPdcD5f5f8od+33qi0oLdtSuj32MmPM1LPnVMyYQhuG9jpdTbkw+0hErNT5ZESsTpkd7Ny2h1uTK7T+B89SnmpP4Pmt8mTmvZQWqffUsWKtfSxHmQ3sRR3rtz6Y9rOUaaI/PoTjmkV9D5wHbBZlVsTW/henzG42WJ1dS/dnxpTt1O+vr+/xdp+mnJ+z1PMg/BxYMSImdzx+UP0+Uh+E3Jql8aiOx1stioNppdqz4/f9KefxUOvrJ8APaxBruYHyfmkvx3Tm3PXhcP/WDHd96Vnj5R+wpOemyRHxn/pzD2Ug+F6Uz3tozZp2G+UO98ERsQBlMO4alAvkJyhT+z6r9rP/MWVA+aO0TX1dfZjSZegPUWaAe4BywbYm8Mm2GbFOo8zqtBDlwnYJSovTv5l1sHfLRZTJD46jBKTlKBcsC9XnJ/ax3mi7kHIR/vF68fp/lLvJH6TU9VcHWP8iSuvBBRHxE8rU1e9jRgtEv8eZmTdGmZnswzV8tj5DZh9KN7Gb6libIcvM+yPicODzwCW1u+PLKBfal1Eufp+knAfnR8T3Kd30tqNMdDB9oPJ37O+Z2sJ1CfD7iDgDuI5yEb8aZdrp31NmE2tpdY08rHblvJoyNuEbtWXzIUoXpO2Y9RzfjzJr1e9qHU6vx/Yws05k0PJtyvTKn4mIMzPzb30sNxifpQyg/21EHE2py32YcXNiMF3vdomIRSldslpdvg7PzFaI/iJlAPvZ9b10K6Wb3nsp595Fs1Hu1jZ/FBHfobQCvoVyQ+KszJydbQ4oM5+q9fTpiDibMq7o+ZS/c49RbrYMZMcacq8DNqfU11cyszXhx2Dr60jKZyJdWt+3PXWZBZgxOyCU83ODiNgT+EVm9jWeZjCG+7dmuOtLz7KlR9JY+galy9EPKRMGfJTyD3vj1kVInfJ1c0rLy0co/+TeWH8+EFgkIt7Ysd3T6vdzs2Pq58y8hjKw+/eUO6ZHUkLJLm1dg6BclHyOMh3t0ZS75FcB62b9gMsu9qV8psRWlC5a21I+E+Mt9fmN+lhvVNWZs95FuRP8OsqUrztRxj6tV1sH+nM8JZQuR6mbD1Jmj1qZ8sGBgznOvSnTIL++7n9TypiEzrvaQ1ZnUtudMtnF1yih9rvAVpk5PTMvprQELkTpTncAJfyuTRmbNKTXqZ5TK1EuKNehnFNHUT6753Bg/Zz5M4W+QwnFB1CmW/835Rz/C+U1OYIysH57ysXoa2PGrF+XUQLRPyiTIxxE+VDYN/U1KL7OYvd+Zr24HbI64H19ygyBB9f9n8eMLondxvt02prSvfDrlAkk9srMQ9r28SDltTiJcp4eTanLzwHbDjDuqK9yt7Z5CqVev06ZKOUTDO5zsYbjEErX2FdRzsdDKF0W3zyI8TxQPi9pRco59Vpgv8w8sPXkYOsrM79PuTmxMOUc+xJlTMxmOfMHkB5IGb91DPWDdGfXcP/WzIG/VdKzenp7B3NTRpLmHlE+sf63lGmuR+QOrvRcFGXa7CkdU063PutqX2DBvsYNRcShlAv+ZTtnbZSkkWZLj6Qm2ocyY9ElAy0oaUh+AtzaNh6JiHg+pcvVjcOcKEGSRoxjeiQ1RkScQOlytRGwf8eHU0oavh8CJ1DGc51L6TL3Xsq4qb3HsmCS1B9beiQ1yQspExIcT+n7LWkOyszvUcZULAF8hfJBlw9Rpv/+xRgWTZL65ZgeSZIkSY1mS48kSZKkRnNMj0bc008/0/vQQ48NvKC6Wnzx52P9DY91OHzW4fBYf8NnHQ6fdTg81t/wjXQdTpo0saev52zp0Yibb755B15IfbL+hs86HD7rcHisv+GzDofPOhwe62/4xrIODT2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnR5hvrAqj5djjgtLEugiRJ0nPONz+x5VgXYdywpUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoWeIImKDiOiNiO07Hr85Ik4a5DYWiIi7BtjHmcMo40kRsekQ13lzRKxcfz5rdvctSZIkjTfzjXUB5lJ3ANsDZwJExOuAhca0RMO3G+V4bs7Mbca6MJIkSU02/Zlp0PvMiO5j6tRHRnT7nSZMmMACCyw4qvscLEPP7LkJiIhYNDP/C+wEnAa8nPLEjsBHgSeBPwN7AfPXZRYH7mxtqAamo4Ee4AFK+JhFREwCfkRpnVsA2Cczb4yIDwE7AL3AmZl5dNs6E4DjgOXrep/OzMsj4u3AIXWf1wPHA5sCb4iI24DrMnOpiFgVOAZ4BngC2LNu5wzgHuCVddl9Z68aJUmSnnse+fs1PD7l9hHfz557nj7i+2jX0zMPG2+8Kbvttteo7ncw7N42+34GbBMRPcAawNUAEbEEcBiwUWauCzwM7A3sA9ySmW+mhIyWE4APZOYGwIXAAX3sbw1KKNoM+ACwUES8BtgOWBdYD5gcEdG2zh7Af+o+twK+HRHzAd8CtsjM1SgBbApwMXBAZv69o2wfzMz1gWOBr9fHVwB2r2XaPCKWGlSNSZIkaVQCz1jo7Z3OJZdcONbF6MqWntl3OvAd4K/Ab9oeXw64NTOn1t+vADYB5gUuAMjMayNiWn1+ReDYmlUmUFqGurmI0mJzLjAN+DywEvAK4NK6zOJ1mZbXAetFxJr19/mApYCHMvP+WpavAMyclZ71ksy8se04vlR/vrN1fBHxL0rLkyRJkgZhwUkr8viUOygddZqj1dIzHhl6ZlNm/jUiFgI+DHySEnYA/ga8JiIWysxHgfWBP1HO6rWBc2u3sQmtTQE7Z+bfI+JNwIv72OUGwL8yc5OIWBs4gtKF7lZgs8zsjYj9gJuBbes6dwD/yMwjImJB4FPAvcBiEfGCzHwwIo4GTgWmM2vL370RsXJm3tx2HNC0d6gkSdIoWuTla7PwS1cb8TE9R3xwdAOIY3qa60fAezPzTxGxHEBm/iciDgEui4jplO5jB9XlT4mIKylh5Mn62L718fkoYWJ34CVd9nUTcGZE7Et53Q7PzJsi4lLgyoiYH7gO+GfbOscDJ0TEr4FFgGMzc3pEvB+4ICKeAW4AfgesCnwpIv7Wtv6ewLdqF76na9kkSZI0TPPMO4EZ98BHxsSJi4zo9ucmPb293rTXyNrhgNM8ySRJkkbZNz+x5VgXYSaTJk1kypSpAy84+9vv6es5JzKQJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmN1tPb2zvWZVDz9U6ZMnWsyzDXmjRpItbf8FiHw2cdDo/1N3zW4fBZh8Nj/Q3fSNfhpEkTe/p6zpYeSZIkSY1m6JEkSZLUaIYeSZIkSY1m6JEkSZLUaIYeSZIkSY1m6JEkSZLUaIYeSZIkSY1m6JEkSZLUaIYeSZIkSY1m6JEkSZLUaIYeSZIkSY1m6JEkSZLUaIYeSZIkSY1m6JEkSZLUaIYeSZIkSY1m6JEkSZLUaIYeSZIkSY1m6JEkSZLUaIYeSZIkSY1m6JEkSZLUaIYeSZIkSY0231gXQM23y4kfGesiSJIkjZoj3/75sS6COtjSI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGs3QI0mSJKnRDD2SJEmSGm2+sS7AUEXEMsDNwPVtD/8KOA/YMjMPn83tvho4LjM3GMSyWwPXZua9Q9j+ocB9mXlc22O7AA9m5nlDLvBsGmifEXEScGZmXtzx+F7AiZk5bcQLKUmSJM1Bc13oqW7rI5zcOEr7/wiwDzDo0NNNZp40R0ozOvs8GDgFMPRIkqS53vRpz9A7vXdEtj116iMjst1OEyZMYIEFFhyVfc3t5tbQM4uI2IASRD5Bafl5M7AicBiwIbA18DHgGeDKzDwoIl4MnAb0APd12eYCwI+BRYHnA58CJgCrAKdExLp1+6sBSwA3ZeauETEJOBlYrG5757Ztvgo4HdgD2Kbu9w7gQOApYDlKS8sX6rInUYLG3cAy7WEvIs4GvpCZv4+IO4CDM/OsiLgE2BVYp8sxH1r3eTzw7Vr2+4BlgXfUTe8dEQfU494XeB2wFHBmROwJ/IjSNXIBYJ/MvLGfl0aSJGlceei6e/nfnx4cse3v+ZOdB15oDujpmYeNN96U3Xbba1T2NzebW8f0vCYiLm/7emnricy8BziAEjq+AbwHWIQSTt6SmesCL42IjSkh5ozM3BA4p8t+XgksSQkD7wHmy8wLKC1KO1Mu+h/KzI0p4WGtWpZPA+dl5jrA/sAadXtBCTw7ZubNHft6BfBOYK1afoAjgSNq+a7qUr6zgc0iYlngSeCtEbFoLdfjfRxzy5bAEpm5BrA7sHTbc3/IzI2AY4BdMvP7lGC0fT2WB4DNgA8AC3UplyRJ0rg1koFnNPX2TueSSy4c62LMFebW0HNbZm7Q9vXPjufPAV4G/Doz/wG8CpgEXBgRlwOvoQSaFYDr6jqzhIrMvJXSInIGcCyz1tfjwAsj4oy63MKUlqAArqnbuDozT6vLb0ZpMXqmyzH9MTOfzsxH63ahtFRdXX/+TZd1fg5sDGwKfJkSSDarj/d1zC0rtpVxCqW1qeUP9ft9tbztLqLU1bnA4cD0LuWSJEkatxZe4QWlL85crqdnHjbZZPOxLsZcoTHd2zrsD1wCrBERawF/Ae4BNs7MaXUw/43Aq4G1gZuA1Ts3EhGvAyZm5ha1K9zVwPmUC/15KAFj6czcrnZp25ryFrq9bu+miHgzsAUlyBxVy3Jy7Y7Xrlun0ltq+S6itADNJDMfiojHgO0oXeW2pYw32gl4pI9jnty27fcCR0XE4pQA2F9ZWse8AfCvzNwkItYGjqB0H5QkSZorLL7GS1h01ReN2JiewzY5eES228kxPYPXuNATEasBO1DCwnLAz+rPXwd+HRHzAndRxup8HjgtIrYH/tZlc38GDomId1Mu+D9bH7+aMqh/S+AzEXEFJSj8FXgJJQj8ICJ2qo/vTh3Xk5m/jIhtKWN4BnJg3c7Hgf/SfRKBc4FdM/PBiPgF8P7M/Euti27H3HIBpWvc1ZQWncf62H7Lb4ALKV3wzoyIfSnnz2zNlidJkjSW5pkw74hte+LERUZs25o9Pb29I5NwNXwRsSNlauw7I2IPYJ3M3G0ObfvVwCqZeWZELAHcCrwiM5+cE9tvt8uJH/EkkyRJzxlHvv3zY12EcWnSpIlMmTJ1JLffZ6fFxrX0NMw9lFaVxyjjgHafw9v+ckR8FJgXOHAkAo8kSZI01gw941hmXkGZFW4ktv0osNVIbFuSJEkaT+bW2dskSZIkaVAMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIazdAjSZIkqdEMPZIkSZIarae3t3esy6Dm650yZepYl2GuNWnSRKy/4bEOh886HB7rb/isw+GzDofH+hu+ka7DSZMm9vT1nC09kiRJkhrN0CNJkiSp0Qw9kiRJkhrN0CNJkiSp0Qw9kiRJkhrN0CNJkiSp0Qw9kiRJkhrN0CNJkiSp0Qw9kiRJkhrN0CNJkiSp0Qw9kiRJkhrN0CNJkiSp0Qw9kiRJkhrN0CNJkiSp0Qw9kiRJkhrN0CNJkiSp0Qw9kiRJkhrN0CNJkiSp0Qw9kiRJkhrN0CNJkiSp0Qw9kiRJkhptvrEugJrvwp13HesiSJIkDdnqXzt6rIugOcSWHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNZuiRJEmS1GiGHkmSJEmNNl9/T0bEBsCPgduAHmB+YN/MvGEkChMRuwAPZuZ5s7n+MsCZmblW22P7AEsBxwGfzcz3z4Gi9leG+zJzqY7HdmGIxxURJ1GO5eK2xxYEvgO8BHg+cB+wd2Y+EBFbA9dm5r1D2MehwH2ZedxwyipJkiSNZ/2GnupXmbk9QERsAnwOePtIFCYzTxqJ7dZt3weMaODpZ98nzaFN7UoJKbsARMRHgc8CH6lf+wCDDj3djORrIEmSNBKemj6dZ3p75/h2p0595Nmfn/e86Uyd+r85st0JEyawwAILzpFtaXAGE3raLQ7cDxAR6wOHULrILQzsAGwALJ+Zn4iIeYEbgdWBPevzvZTWi6MjYhvgQGAa5UJ9e8oF/H3ACcDxwNLAi4HzMvPTtfXjSWCZ+vgumXn9YAre3gpUy/4F4BngL8DewI7AbvV4DgFWBLYBFgL+A2xdj6F9mWWAfYF5axkPAeaPiNOBlwMPANsCn6rHdTxwDLAG8Ly6jfO7HWsfh/FvYI+IuAr4dd1WT0RsAawCnBIR6wKHAasBSwA3ZeauETEJOBlYjNJqt3Nb3bwKOB3Yox7zfcAdlNfnKWC5WndfqMueRHnd7gaWycwNBqp/SZKkkXDpww9y46NzJozMYs+dB15mNvT0zMPGG2/KbrvtNSLb16wGM6Zno4i4PCKuAU4EzqyPvxbYqV7wngW8CzgDmFwDz6bAZZQL5u2AdYH16vMBvAc4MjPXpVz4L9K2z6WB32bm2ygBYZ+25+6ujx8DdDtTXlPLe3lEXA58rP3JiOihhKptMnN94J/ALvXph2p5LqMEhrdm5pqUcLh6xzJ/BA6qx/QGSthZmBIAD67LLAqs2rb7ycCSmbkGsCElmPR3rDPJzJ8Bnwd2B/4GXAqsmJkXUALmzsACtYwb1+2vFREvBT5NCVTrAPvXfQEEJfDsmJk3d+zyFcA7gbWAA+pjRwJHZOaGwFV9lVWSJGk0jFjgGUG9vdO55JILx7oYzylD7d4WwDX1IvqfwNER8T/gpcBVmTk1In4NvI3SFetwYCXKxfOldXuLA8tTwsgnI+JDwO3AOW37fBBYPSI2BB6hjCVqaY0nugd4U5fy3tbe8tA2pqdlEqVF5cflcFgQ+CVwJ5AAmTk9Ip4CzqjH9zJgQl0/6/flgFsy8/H6+0F1fw9m5l31sfsoY2+eLQ5wTd3HQ8BnImKRfo51JhGxNnBpZp5Vg+V7Ka0ub2xb7HHghRFxBvA/SgibUPf9g7rvq4Gr65iezYCnKa1enf6YmU8DT0dE6zhXBK6uP/+G0kImSZI0JlZZaGFuevR/zPnObSOn1dKj0TPU7m3/bvv5BOCVNeicTOky1Xr8QEqLxs21ZeVWYLPM7I2I/YCbKa00h2bm/RFxPKX7WMsuwMOZuXftTrVX3Q4w7HP6P8A/gK0y878RsSUlHLwcmA4QESsDkzNzzYh4PvCHtuObXr//BXh1RMyfmU9GxE8p42r6K9/tlBYxImJRyiQRF/RzrJ3eU8t/eGY+ExE3U7r7tco1DyXELJ2Z29UubVvXst9Oaa26KSLeDGxBCUhH1WM5uU5c0a7bsdwCrA1cRGkBkiRJGjNvWewFrLfIYiMypmfVz33x2Z+XWGJhHnjAMT1zq8GEno1qN7FngInAxzLz8Yg4FfhNRDxKCUMvAcjMa+vF+7fr7zdFxKXAlRExP3AdpZXoOuD8iJhKCR3nAx+q+7wUOL22bDwJ/Lm1/eGqrTgfAS6IiHkorSs7U0JPy53Ao3XsDMC/OvefmVMi4svAryOiF/h5Zv6zth715TzgrRFxJaXuDwP+zuCP9VPAtyLiRuDR+rV7fe5q4BRgS0oL0hWU0PLXur0jgB9ExE718d3rcZOZv4yIbSlhdSAH1u18HPgvZWyPJEnSmHnePCPzKSwTJ84YfbHoohN56ik/7WVu1dM7h1NxDRJXAW/LzEcGWl5zl4jYkTI19p0RsQewTmbu1t86F+6869zU4ixJkgTA6l87+tmfJ02ayJQpU8ewNHO/ka7DSZMm9tVbasjd2/oVEcsCZwMnGnga6x7gzIh4jNL6t/sAy0uSJEljao6Gnsz8G2XqZDVUZl5BmRVOkiRJmivYMVGSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSo8031gVQ821+yolMmTJ1rIsx15o0aaL1N0zW4fBZh8Nj/Q2fdTh81qGey2zpkSRJktRohh5JkiRJjWbokSRJktRohh5JkiRJjWbokSRJktRohh5JkiRJjWbokSRJktRohh5JkiRJjWbokSRJktRohh5JkiRJjdbT29s71mWQJEmSpBFjS48kSZKkRjP0SJIkSWo0Q48kSZKkRjP0SJIkSWo0Q48kSZKkRjP0SJIkSWq0+ca6AGqGiJgHOBZ4PfAksEdm3tn2/J7A3sDTwOcz8/wxKeg4NlAd1mUmAVcBK2fmE6NfyvFrEOfgfsD29dcLM/Ow0S/l+DaIOvwAsAvQC3w1M388FuUczwb5Pp4HuAA4NzOPG/1Sjl+DOAe/CawLTK0PbZWZ/x31go5jg6jDzYBDgB7gD8AHMtPPL2nTXx1GxCrAUW2LrwVMzsyLR7mY49YgzsH9gR2A6cARmXn2aJTLlh7NKZOBBTJzbeAg4GutJyJiKeDDwJuAtwFfjIj5x6KQ49xk+qhDgIh4G3AJsNToF22uMJm+z8HlgB2BdSj/oDaJiJXHopDj3GT6rsMlgX0pdfgW4GsR0TMWhRznJtPP+7j6PLD4aBZqLjKZ/uvvjcDbMnOD+mXgmdVk+n4fTwSOBN6emWsCdwFLjkEZx7vJ9FGHmXlj6/wDvg38zMAzi8n0fQ4uBnwEWBvYhJkD5Igy9GhOWRe4GCAzfwus1vbcGsBVmflk/Qd1J+AF56z6q0Mod0TeCjw4yuWaW/RXf/cAm2bmM/WO5gTAlrJZ9VmHmfkfYJXMnEYJ3k94d7irft/HEbEt5b3sRVJ3fdZfvXu8PPDdiLgqInYbmyKOe/2dg+sAf6TctPgN8O/MnDL6RRz3Bvp/TEQsBBxGuYDXzPqrv0eBu4GF6tf00SqUoUdzyiJA+x23ZyJivj6emwosOloFm4v0V4dk5i8z84HRL9Zco8/6y8xpmfmfiOiJiK8CN2Tmn8aklOPbQOfg0xHxQeC3wKmjXbi5RJ91GBErUbp0fHYsCjaX6O8cXAg4BtgJ2BR4vy22XfVXh0sCGwIHApsBH42IFUa5fHODfv8WVrsDP6k3hDSzgervHuA24Hrg6NEqlKFHc8ojwMS23+fJzKf7eG4i8PAolWtu0l8damD91l9ELACcVpd5/yiXbW4x4DmYmd8CXgy8OSI2HM3CzSX6q8OdgZcCv6KMjfpYRGw6usUb9/qrv8eAb2bmY5k5lVKPrx/tAs4F+qvDB4DfZeZ9mfk/4ApglVEu39xgMP+PdwS+N3pFmqv0V3+bUf6HLAu8HJgcEWuMRqEMPZpTrgI2B4iItSjN5y3XAetFxAIRsSiwInDL6Bdx3OuvDjWwPuuvjj05F7gpM/fOzGfGpojjXn91GBFxVq3LaZTBqaPWLWEu0mcdZuYBmblmHQtwEvB1xwLMor+/gysAV0XEvBExgdKF5vrRL+K4118dXg+sFBFL1jvva1HuuGtm/f4/rtcy82fmPWNQtrlBf/X3EPA48GSdkOlhYLHRKFRPb69dsjV8bTN1rEyZEWZXygl/Z2aeV2dv24sStI/IzJ+NWWHHqYHqsG25u4BXO3vbzPqrP2Be4AxKt6yWT2bmNaNdzvFsEO/jQyh36XqBizLz8DEr7Dg1hPfxocB9zt42s0Gcg58A3k0J3qdYf7MaRB1uD3yiLv7jzPzy2JR0/BpEHa4OfCozJ49dKcevQdTfYZQuqtOBK4EDRmOMqKFHkiRJUqPZvU2SJElSoxl6JEmSJDWaoUeSJElSoxl6JEmSJDWaoUeSJElSoxl6JGmciYhlIqI3IjbuePyuiFhmDu/rsIhYr/78vYhYbU5ufzRFxLibjjQiToqIXSLiJRFx4QDLXtb2841zuBwbRMTlc3B7zx5PRLwjIj42xPWPjIhVa92cNKfKNYT9XxgRLxnkskM6r9rrIyLWiIghTQkdEVtHxAeHso6kgRl6JGl8mgacEBETB1xyeNanfI4RmblHZv5+hPf3nJSZ92bm5gMstkHb8quMaIGGqeN43ggsMth1I+L1wIsz84YRKdwgZObmmXnvCG2+vT5eA7xoKCtn5tnANhHxwjldMOm5bL6xLoAkqat7gV8CX6N8sO9MIuIgyoc0zgv8AjgwM3sj4sPAhyifcn0H8JfMPLTeOX4vsBDlA+G2A1YHVgO+FxFbA8cAhwIfBk7PzJ/Wff2+luER4DvAEsBjwIc6L1zrXftHgXUpn7L90brf1wPnZOb+ETEvcCTlIn9e4KTM/Eb9hPjvACtRLhQT2Kb+fDZwC7Aq8G/gXZn5YLeKi4jnAyfUfU4HvpqZp0TEBOC4WrZ/Uj5k9XOZeXnbuhsAh1FC59LAdcAewIuBi4H/AE8Ab+vjGHoor9nbKa/hvMDltYXu8sxcJiJeAZwIvLDW4x71i4i4NjPXrK0LE4C/A6tm5r8j4gW1Dl4BvAU4vC7zN2DPzHygox42Ab5Ry3tH2+OvosvrWF+7/1Iu2l8GHJaZJ0bEW4Cv1Pp6CHgPsDBwOeUDB/ep270H+AywSWb+KSIWqvtdvuPDlPcHTu3yuq0FfBNYoNbz3vU1fHdmbhcRywN/Apaq9XEx8FngwX6OZwngVZQPP/x5277uqq/dIsB3KddDTwC7Zuafu5Ttu8AatVy7Zebfu9Uj8GRbffwX2A9YOCI+BXyJ7ufMBrV+5wVuycz3AWcBHwAO6SyLpNljS48kjV/7A2/r0s1tU8qF6eqUEPBSYMeIWJlyofRGYD1g+br8IsBkYIPMXAk4B3h/Zp4C/B7YIzP/2LaLHwLb13WXBxbMzOuBkykXj2+ghKAz+yj3SzLz9ZQL0hMpF4GrAHtGxKLAngB1O2sAW9UudusAT2Xm2pQL1QUpF9VQLn6/Xsv/MLBjP/V2KPBAXXYj4NBaN/tQQt+rKZ8Qvnof669BqcdXUy7AP1AfD2CnzHxrP8fwTspr8lrgXfU4Oh0L/KyW71Dg05n54bq9NVsLZebTwE/qdqjbPocSJr8EvC0zV6WE3pm6UEXE/JTXa9vMfCPweNvT/b2OS1POnXcAX62PfRrYJzNXA34OvKGtjLdRguRxmfn9uu2d2sp7fnvgqaHw7cBvOsr7vFqOD9Zz5zjgDErwX7eu9xbgfmD9iFiQ8nr8boDjeSAzV2wPPB32A75Wj+0YYK0+lvt1bX07ixLM6Lbfjvr4BuU9cF5mfoG+zxmAFYCNauABuALYso+ySJoNhh5JGqcy8xHKhVJnN7e3AmsCfwCup7TWvLY+fn5mPlIvNM9o284OwPYR8UXKBe3C/ez6AmCtus/3AKdFxMKUkHBiHW9yOuUO9hJd1r+ofr+bcuf6/sycSrkjv3gt55Z1O9dSWhVel5lXAMdGxAcoF5bLt5Xz/rZWpVuAF/RT/o2A79dj/w9wLuXu+sbAaZnZm5l3A5f2sf4VWfRSAuBGbWW4q/7c9Rjqfs7KzGmZOQXoNo5n/bpdMvPCzHx3P8fybAClvBanUl77lwOX1f1/kBpw27wOuDczb6+/nwwwiNfxknrc7XV8HnB2RHwLuD0zL+mnvCdSzjWA9wEndTy/ZD3uRzseXwF4KDN/V5//CSUw9lBai1amvA5HUepvQ+AySojt73iu7aesUM71b0XE94Gn6vqdHs/M0+rPpwIbDPH90NLXOVMPOf/btuzdzPqaShoGu7dJ0jiWmZdERKubW8u8wFGZ+XWAiFgMeBrYnS43syJiaUpXpG9RAsl9lNaIvvb5VEScT7nT/G5gi7rPJ9rHmkTEyyhBptNTbT8/3eX5eSl3yM+q21kSeDQitqR02fom5eJ5ScpFL5SuRy29bY9301kHPZT/d890ea6b9jLP0/Z7e2tJ12OgdFNq30e345/W+qG2YKxYWwhmkZm/j4gXRMTqwMsy8+qI2Aq4MjO3rNtYAOgc+9XbRzkGeh2fqPvtjYhWGb4RET+ntNB8JSJ+CrRCQGd574qIuyNiG+BFmdkZOqbTvU66vS49tbwXUgLrq4F9KWFnOnD+II6n/TXrVt6fRsQ19dg+SmlZ3LNjsWc6yjRtEPvtpq9zZs0u5ZxWj1HSHGJLjySNf/tTxpC0Zpv6FfDeiFi4joM5B9iW0nKxeUQsUrsLvZNy8bs6cGftbnMtsBl18gLKBWi3G2A/rPt9MDPvrneh/xwROwHULndXzObx/IrS1W1CvWN+JeXC763AjzPzREowe3NbOYe6/d1rOZekdO27nNJVavuI6Kkzd21AqZ9O60bESyNiHmBnZrRcDeYY/g94V0TMHxGLA5t2WfcKZrTevJUypgTgmfp6djoNOJ4Z3bauBdaOiBXq75+hjBVpdzPwwjppAJRWImbndYyIa4GJmXkUZYzQGzoW6TyHfgAcTW3NalfHHc1T62ymp4AlargjIt4N3F3HbV1A6Zp4W11/GqW18pLhnpcR8SNgjcw8nlKPnccGpQWn1dVsN+D/Bthve320/9zXOdPNssCdgz0OSQMz9EjSONfWzW1C/f3nwM8oF7+3ADcCJ2fmLZSLzWsoYyamUu4gX0K50LwN+C1wF+WiCsrg/OMiYp2OfV4FLMrMA853BPaIiJuBLwLb1a5QQ3Uc8GfgBsqYohPrZAInAO+JiBsoYyd+21bOoTgceEFE/JFyIfqFOibpBEqd/JHS3etuurcE3AucAtxGmfDge4M9hsw8lxKwbqF0C+vWgvNB4J21m9NhzJio4lzgptpy0+5UypioUwEy8z7KxfeP6zG+gRJQn5WZ0yhB54cRcT3w/Lanh/o6HgycFBF/qGXtHFx/BWVM2Yfq72dRusbNEnqqiyiBtr28T1Im1/hWRNxCqaPt6nN3UFpYLq+LXw78KTP/N5vH0+4I4OBaR18Fuk29/TAwOSJuorQ47TfAftvr4zpKV9Ev0fd5382GlPNB0hzS09s77j7WQJI0G+qd/y1qiw4RcS7wvX4GcT+nRMQWQE9mnl8nVLgBWC3bZoGrM2kdmpkbjE0p5261u95mlIkPug7Er61Pn87Md3V7XhARVwLbZOb9Y10WqSls6ZGk5rgbWD0ibqktAH+mjHtQcRtwUG1h+TXw2exj2mvNtm9QZkH7eF8LZOZNwD0R0ee4sueyiNgW+KmBR5qzbOmRJEmS1Gi29EiSJElqNEOPJEmSpEYz9EiSJElqNEOPJEmSpEYz9EiSJElqNEOPJEmSpEb7f2Ez0el3MklkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "neg_log_pred_densities = np.vstack(\n",
    "    [\n",
    "        -dist.Bernoulli(logits=bhs_predictions).log_prob(y_test),\n",
    "        -dist.Bernoulli(logits=model_selection_preds).log_prob(y_test),\n",
    "        -dist.Bernoulli(logits=fixed_weights_preds).log_prob(y_test),\n",
    "    ]\n",
    ").T\n",
    "neg_log_pred_density = pd.DataFrame(\n",
    "    neg_log_pred_densities,\n",
    "    columns=[\n",
    "        \"Bayesian Hierarchical Stacking\",\n",
    "        \"Model selection\",\n",
    "        \"Fixed stacking weights\",\n",
    "    ],\n",
    ")\n",
    "sns.barplot(\n",
    "    data=neg_log_pred_density.reindex(\n",
    "        columns=neg_log_pred_density.mean(axis=0).sort_values(ascending=False).index\n",
    "    ),\n",
    "    orient=\"h\",\n",
    "    ax=ax,\n",
    ")\n",
    "ax.set_title(\n",
    "    \"Bayesian Hierarchical Stacking performs best here\", fontdict={\"fontsize\": 18}\n",
    ")\n",
    "ax.set_xlabel(\"Negative mean log predictive density (lower is better)\");"
   ]
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   "source": [
    "So, in this dataset, with this particular train-test split, Bayesian Hierarchical Stacking does indeed bring a small gain compared with model selection and compared with fixed-weight stacking.\n",
    "\n",
    "### 4.3 Does this prove that Bayesian Hierarchical Stacking works? <a class=\"anchor\" id=\"4.3\"></a>\n",
    "\n",
    "No, a single train-test split doesn't prove anything. Check the original paper for results with varying training set sizes, repeated with different train-test splits, in which they show that Bayesian Hierarchical Stacking consistently outperforms model selection and fixed-weight stacking.\n",
    "\n",
    "The goal of this notebook was just to show how to implement this technique in NumPyro."
   ]
  },
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    "## Conclusion <a class=\"anchor\" id=\"conclusion\"></a>\n",
    "\n",
    "We've seen how Bayesian Hierarchical Stacking can help us average models with input-dependent weights, in a manner which doesn't overfit. We only implemented the first case study from the paper, but readers are encouraged to check out the other two as well. Also check the paper for theoretical results and results from more experiments.\n",
    "\n",
    "## References\n",
    "\n",
    "1. Yuling Yao, Gregor Pirš, Aki Vehtari, Andrew Gelman (2021). [Bayesian hierarchical stacking: Some models are (somewhere) useful](https://arxiv.org/abs/2101.08954)\n",
    "2. Måns Magnusson, Michael Riis Andersen, Johan Jonasson, Aki Vehtari (2020). [Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data](https://arxiv.org/abs/2001.00980)\n",
    "3. https://github.com/yao-yl/hierarchical-stacking-code.\n",
    "4. Thomas Wiecki (2017). [Why hierarchical models are awesome, tricky, and Bayesian](https://twiecki.io/blog/2017/02/08/bayesian-hierchical-non-centered/)"
   ]
  }
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